Methods of detecting osteoarthritis and predicting progression thereof

ABSTRACT

Provided herein are methods and biomarkers useful for detecting and diagnosing osteoarthritis and predicting the progression of osteoarthritis in subjects. The diagnoses and predictions of prognosis may be used to develop treatment plans for subjects. Also included are methods of treating subjects and administering pharmaceuticals based on the diagnosis and prognosis predictions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a national stage filing under 35 U.S.C. 371 of International Application No. PCT/US2016/016736, filed Feb. 5, 2016, which claims the benefit of priority of U.S. Provisional Patent Application No. 62/112,445 filed Feb. 5, 2015, and U.S. Provisional Patent Application No. 62/172,394 filed Jun. 8, 2015, all of which are incorporated herein by reference in their entireties.

SEQUENCE LISTING

A Sequence Listing accompanies this application and is incorporated herein by reference in its entirety. The Sequence Listing was filed with the application as a text file on Feb. 5, 2016.

INTRODUCTION

Osteoarthritis is a prevalent chronic disease that represents a large and growing global health burden of large unmet need with respect to diagnostics, prognostics and therapeutics. Based on data from the Global Burden of Disease 2010 Study, musculoskeletal conditions are the second greatest cause of disability, as measured by years lived with disability (YLDs) worldwide and across most regions of the world; the main contributors are low back pain (83.1 million YLDs), neck pain (33.6 million YLDs) and osteoarthritis (17.1 million YLDs) with osteoarthritis of the knee accounting for 83% of this total. Globally, osteoarthritis of the knee affects 251 million individuals, and back and neck pain (likely largely also attributable to osteoarthritis) currently affect 964 million people worldwide. In the US, according to the Centers for Disease Control, osteoarthritis affects an estimated 26.9 million US adults (estimates from 2005, up 28% from the estimated 21 million US adults impacted in 1990). As the US population continues to age and struggle with obesity, the incidence and prevalence of the disease is expected to continue to grow. Consequently, the annual cost of osteoarthritis to the US, estimated to be $89.1 billion in 2001, is anticipated to continue to grow.

Demographics and baseline characteristics are poor predictors of OA progression including age, sex, body mass index, knee pain, general bone mineral content, and joint space width at baseline. In a systematic literature review, it was noted that 25-75% of painful knees cannot be diagnosed as OA by x-ray. Bedson J and Croft P R, BMC Musculoskelet Disord 9:116 (2008). Moreover, knee pain has been reported to have only a 23% sensitivity and 88% specificity for the diagnosis of radiographic OA. Hart et al., Ann Rheum Dis 50(7):467-70 (1991). Bedson et al concluded that, “The results of knee x-rays should not be used in isolation when assessing individual patients with knee pain.” Bedson J and Croft P R, BMC Musculoskelet Disord 9:116 (2008). Our goal was to develop a better means of diagnosing and predicting progression of knee osteoarthritis.

SUMMARY

Methods of diagnosis and predicting progression of osteoarthritis, and in particular knee osteoarthritis, are provided herein. In one aspect, methods of diagnosing osteoarthritis by measuring biomarkers are provided. The methods include measuring the level of a biomarker in a sample from the subject. The biomarker may be at least one biomarker selected from the group consisting of CRAC1 (CRTAC1), A2AP, A1BG, A2GL, AACT, ACTG, AMBP, APOB, APOE, B2MG, C1QC, C1R, C1RL, C4BPA, C4BPB, CD14, CD44, CERU, CFAB, CFAH, CFAI, CILP1, C1S, CNDP1, CO2, CO4B, CO5, CO6A3, CO8B, CO8G, CO9, coll3, COMP, CTX1a, CTX1b, CTX2, CTXi, CXCL7, ECM1, FA12, FA5, FBLN1, FBLN3, FCGBP, FCN3, FETUA, FINC, GELS, HA, HABP2, haptoglobin, HEMO, HEP2, HGFA, HRG, hyaluronan, IC1, ITIH1, ITIH4, KNG1, LAMA2, LUM, LYAM1, MASP1, PCOC1, PGCA, PHLD, PLF4, PLMN, PRG4, RET4, SAMP, SHBG, TENX, TETN, THBG, TIMP1, TSP1, TSP4, VTDB, VTNC, ZA2G, ZPI, or any combination thereof. The levels of the biomarker in the subject are then compared to the levels of the biomarker in a control subject or a reference level of the biomarker. The subject can then be diagnosed with osteoarthritis if the expression of any of the biomarkers is altered as compared to the reference level.

In another aspect, methods of predicting progression of osteoarthritis by measuring the expression levels of biomarkers in a sample from a subject are also provided. The biomarker may be at least one biomarker selected from the group consisting of A1BG, A2AP, A2GL, AACT, ACTG, AFAM, ANT3, APOB, APOH, B2MG, C1QC, C1R, C1RL, C4BPA, C4BPB, CD14, CD163, CD44, CERU, CFAB, CFAH, CFAI, C1S, CO2, CO4B, CO5, CO6A3, CO8B, coll3, CRAC1 (CRTAC1), CTX2, CXCL7, DOPO, ECM1, FA5, FA12, FBLN1, FCGBP, FCN3, FETUA, FINC, GELS, HABP2, haptoglobin, HEMO, HEP2, HGFA, HRG, hyaluronan, ITIH4, KLKB1, KNG1, LUM, LYAM1, PGCA, PHLD, PLF4, PLMN, PRG4, RET4, SAMP, TENX, TETN, THBG, THRB, TIMP1, TSP1, TSP4, VTDB, VTNC, or combinations thereof. The level of the biomarker in the sample is compared to a reference level of the biomarker. The comparison is then used to predict the progression of the osteoarthritis. A significant alteration in the level of any of the biomarkers as compared to the reference level is predictive of progression of osteoarthritis or indicative of risk of osteoarthritis progression.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing how the biomarkers could be used to assist in diagnosing and predicting the progression of knee osteoarthritis and how this would be translated into a treatment plan.

FIG. 2 is a dot plot showing the coefficients of variation for each of the tested peptides and those colored blue and clustering near the log based line were included for further analysis and those in red were not included in the dataset.

DETAILED DESCRIPTION

Methods of diagnosing or predicting progression of osteoarthritis in a subject are provided. The methods all rely on detecting or determining the level of at least one biomarker or combinations of biomarkers in a sample from a subject. In some cases, the subject has knee pain or has already been diagnosed with knee osteoarthritis. The subject may also be diagnosed with, or suspected of having osteoarthritis in another joint other than the knee, such as the hip, back, hand, elbow, shoulder, neck or other joint in the subject. Suitably, the subject is a human, but subjects may include other non-human mammals such as domesticated animals.

Thus, the present methods permit the diagnosis and personalization of therapy or a treatment plan, wherein a subject's biomarker profile is predictive of, or indicative of, a diagnosis of osteoarthritis or risk of progression of osteoarthritis. The methods disclosed herein related to osteoarthritis can be used in combination with assessment of conventional clinical factors or measures, such as age, sex, body mass index or radiographic parameters; this is analogous to the practice for diagnosis or prognosis of rheumatoid arthritis by measuring rheumatoid factor and/or anti-cyclic citrullinated peptide and considering them in conjunction with morning stiffness, joint swelling and/or radiographic features of joint disease, etc. In this manner, the methods of the present disclosure permit a more accurate evaluation of osteoarthritis both at the level of diagnosis and of prognosis of progression of the disease.

In some embodiments, the method includes determining the levels of the biomarkers provided herein in Table 2a in a sample from a subject diagnosed with or suspected of having osteoarthritis. Biomarker levels in some instances may be normalized against the levels of all proteins in the sample, or against a reference or normalization protein(s) in the sample as discussed and exemplified in the Examples. The following set of peptides may be used as normalization peptides in the methods provided herein: TSP1, CNDP1, FA5, SHBG, PLF4, C1QC, ADIPO, APOA4, ACTG, CD14, K2C1, CBG, CHLE, FA11 or any combinations thereof. The level of the biomarkers is indicative of the prognosis for the progression of osteoarthritis in the subject or may be used for the diagnosis of osteoarthritis and may be used to develop a treatment plan or determine the effectiveness of a particular treatment.

In some embodiments, the methods disclosed herein further comprise measuring the level of at least one normalization peptide from a protein selected from TSP1, CNDP1, FA5, SHBG, PLF4, C1QC, ADIPO, APOA4, ACTG, CD14, K2C1, CBG, CHLE, FA11 or any combination thereof in a sample from a subject and normalizing the level of the biomarker in the sample from the subject and the reference level of the biomarker to the level of the normalization peptide in the sample and the reference prior to comparing the level of the biomarker in the sample to the reference level of the biomarker. In some embodiments, the normalization peptide comprises at least one of the sequences of Tables 12 or 13.

The early stages of osteoarthritis are characterized by pain in the affected joint and changes to the cartilage that lines the ends of the bones and cushions the joints. The changes may include thinning of the cartilage layer. Usually the pain in the early stages is well-managed with rest and topical or non-prescription ingestible anti-inflammatory pharmaceutical agents. Moderate osteoarthritis often involves pain with standing as well as when moving and may show bone thickening along joint margins. Treatment involves increased use of anti-inflammatory and anti-pain medications including NSAIDS and steroids. Progression to later stages may restrict the ability of the affected subject to move and to work and may require treatment with stronger pain medications such as opioids, injectable corticosteroids, lubrication injections, physical therapy and joint replacement surgery. Identifying patients likely to progress to a more severe form of the disease would aid medical professionals in determining the appropriate treatment options for individuals with osteoarthritis earlier in the disease course, before disability occurs and when the disease is more likely to be favorably impacted by treatment.

The methods of the present disclosure can also be used to assist in selecting appropriate courses of treatment and to identify patients that would benefit from a particular course of therapy. As shown in FIG. 1, if a subject is demonstrated, via evaluation of the biomarkers provided herein, to be likely to have non-progressive disease then the treatment plan on the left can be pursued which is non-invasive and includes non-pharmacologic therapy. If instead the subject is determined to be likely to have progressive osteoarthritis, then more aggressive treatment options can be pursued including injections or treatment with pharmaceuticals and surgery. Thus, the levels of the particular biomarkers described herein provide insight into which treatment regimens will be most effective for the subject. This information can be used to generate treatment plans for the subject to prolong an active, pain-free lifestyle and minimize side effects, adverse reactions or therapy related toxicity. Methods of developing a treatment plan for a subject with osteoarthritis are also provided herein. Treatment plans may be developed as shown in FIG. 1 using the predictions of the likelihood of progression of osteoarthritis. Methods of monitoring a treatment plan are also provided. The methods may be performed on a recurring basis in order to determine whether a particular treatment plan is effective in reducing and maintaining reduction of at least one symptom of osteoarthritis.

In some embodiments, the methods of the present disclosure may further include administering an anti-inflammatory or anti-pain therapeutic to the subject if the subject is diagnosed with osteoarthritis or predicted to have non-progressive or progressive disease. Suitable anti-inflammatory therapeutics are known to those skilled in the art and may include, without limitation, nonsteroidal anti-inflammatory drugs (NSAIDs), disease-modifying osteoarthritis drugs (DMOADs), disease-modifying antirheumatic drugs (DMARDs), corticosteroids, and hyaluronans. Several classes of DMARDs may be used in accordance with the present invention including, but not limited to, traditional DMARDs such as methotrexate, hydroxycholorquine, sulfasalazine, leflunomide, cyclophosphamide and azathioprine; biologics such as anti-IL-1 therapeutics, anti-TNF therapeutics, metalloproteinase inhibitors, p38 inhibitors, abatacept, adalimumab, anakinra, certolizumab pegol, etanercept, infliximab, golimumab and rituximab; and JAK inhibitors such as Tofacitinib. Suitable anti-pain therapeutics include, without limitation, non-opioid analgesics (e.g., acetaminophen), nonsteroidal anti-inflammatory drugs (NSAIDs), opioid analgesics, and co-analgesics and most likely in future, nerve growth factor inhibitors. Subjects having stable or non-progressive disease may be treated with topical or ingestible pain or anti-inflammatory medications. Subjects identified as having progressive destructive or aggressive disease likely to lead to joint destruction may be referred for injectable lubricant or biologic agent procedures, stronger pain medications such as opioids, bone-acting agents such as calcitonin, bisphosphonates and hormonal therapies, physical therapy, arthroscopic surgery, osteotomy, fibulectomy or joint replacement surgery.

Methods of treating osteoarthritis in a subject are provided. The methods of treating osteoarthritis may include administering a therapeutically effective amount of an anti-inflammatory or anti-pain therapeutic to the subject provided that the levels of at least one of the biomarkers listed in Table 2A in a sample from the subject was determined to be modified (increased or decreased) as compared to the reference level as shown in Table 2A or greater than/less than the threshold values reported in Table 2A, 2C or 2D to diagnose osteoarthritis or indicate the subject's disease is likely to progress.

In some embodiments, the age, gender and/or body mass index of the subject are also used in making the prediction of progression or diagnosis. In some embodiments described herein, diagnostic and prognostic performance of the biomarkers and/or other clinical parameters such as demographics including sex, age, BMI and cohort were assessed utilizing logistic regression to compute p-values and confidence intervals. These statistics were then used to calculate a Benjamini-Hochberg FDR threshold. A biomarker was considered a significant biomarker if the FDR passed 10%. Knee-level analysis required a paired evaluation and the generalized estimating equation method was used to account for the correlation structure and the significance of the biomarker was assessed by a Wald statistic. The statistical analysis used is described in the Examples section. Methods for assessing statistical significance are well known in the art and thus other methods may be used. In some aspects of the invention, a p-value of less than 0.05 constitutes statistical significance.

As used herein, the term “subject” and “patient” are used interchangeably and refer to both human and non-human animals. The term “non-human animals” as used in the disclosure includes all vertebrates, e.g., mammals and non-mammals, such as non-human primates, sheep, dog, cat, horse, cow, chickens, rodents, guinea pigs, amphibians, reptiles, and the like. Preferably and in some embodiments, the subject is a human patient. The subject may be a human patient with knee pain or already diagnosed with or suspected of having osteoarthritis.

The biomarkers of the present disclosure include proteins and genes encoding the proteins. The biomarkers analyzed are provided in Table 2B along with an indication of the commonly used abbreviations for each marker. Such biomarkers include the entire protein or peptide portions of the protein. As shown in the Examples, peptides from each of these proteins were identified as useful in the methods provided herein. The biomarker peptides used in the examples are shown in Table 2A. These biomarkers may be used alone in the methods or in combinations as described below.

Fragments and genetic variants of biomarkers are also encompassed by the present invention. “Fragment” is intended to include a portion of the amino acid sequence and hence a portion of the protein encoded thereby. A fragment or a biomarker peptide will generally encode at least 7, 8, 9, 10, 12, 15, 17, 20, 22, 25, 30 or more contiguous amino acids, or up to the total number of amino acids present in a full-length biomarker. “Variant” is intended to mean substantially similar sequences. Generally, variants of a particular biomarker of the invention will have at least about 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more amino acid identity to that biomarker as determined by amino acid alignment programs.

A “biomarker” is a protein or glycan whose level in a sample is altered compared to that of a normal or healthy sample or is indicative of a condition. The biomarkers disclosed herein are proteins or glycans whose levels correlate with osteoarthritis and can be used to predict the progression of the disease as well as diagnose the disease.

In particular embodiments, the methods for predicting progression of or diagnosing osteoarthritis in a subject include collecting a patient body sample. The sample may or may not include cells. In particular, the methods described herein may be performed without requiring a tissue sample or biopsy. “Sample” is intended to include any sampling of cells, tissues, or bodily fluids in which a level of a biomarker can be detected. Examples of such samples include, but are not limited to, blood, serum, urine, synovial fluid, saliva, or any other bodily secretion or derivative thereof. Blood can include whole blood, plasma (citrate, EDTA, heparin), serum, or any derivative of blood. Samples may be obtained from a patient by a variety of techniques available to those skilled in the art. Methods for collecting various samples are well known in the art. In some embodiments, the sample is serum, plasma, urine, or synovial fluid. In some embodiments, the sample is serum depleted of at least 7 major serum proteins. In some embodiments, the serum proteins depleted are selected from the group consisting of albumin, IgG, IgA, transferrin, haptoglobin, anti-trypsin, fibrinogen, alpha 2-macroglobulin, IgM, apolipoprotein AI, apolipoprotein AII, complement C3, and transthyretin.

Any methods available in the art for detecting the level of biomarkers are encompassed herein. The level of a biomarker of the invention can be detected using a peptide corresponding to the biomarker. “Measuring an expression level of” is intended to mean determining the quantity or presence of a biomarker (i.e., peptide) in a sample for at least one of the biomarkers of Table 2a. Thus, “measuring an expression level of” encompasses instances where a biomarker is determined not to be detectable due to failure to be produced, or due to production below the detection limit of the assay; “measuring an expression level of” also encompasses low, normal and high levels of detection. Measuring an expression level also includes instances where a marker is degraded or is more stable in a person with osteoarthritis or with progressive disease and is not limited to production of new peptide or the timing of peptide production.

Methods suitable for “measuring an expression level of” biomarkers are known to those of skill in the art and include, but are not limited to, ELISA, immunofluorescence, FACS analysis, Western blot, magnetic immunoassays, mass spectroscopy, gel electrophoresis, antibody-based microarrays and non-antibody-based microarrays or combinations of these methods. In the past, the gold standard for detection of growth factors and cytokines in blood was the use of ELISAs; however, multiplex technology and mass spectroscopy offer attractive alternative approaches for protein-based analyses. The advantages of multiplex technology compared to traditional ELISA assays are conservation of patient sample, and significant savings in cost, time and labor. In some embodiments, the biomarker is measured using an antibody-based capture method. In some embodiments, the biomarker is measured using mass spectrometry.

Several multiplex platforms currently exist. The Luminex bead-based systems are the most established, being used to detect circulating cytokines and growth factors in both mice and humans. This method is based on the use of microparticles that have been pre-coated with specific antibodies. These particles are then mixed with sample and the captured analytes are detected using specific secondary antibodies. This allows for up to 100 different analytes to be measured simultaneously in a single microplate well. The advantages of this flow cytometry-based method compared to traditional ELISA assays are in the conservation of patient samples as well as significant savings in terms of cost and labor. An alternative, plate-based system is produced by Meso Scale Discovery (MSD). This system utilizes its proprietary Multi-Array® and Multi-Spot® microplates with electrodes directly integrated into the plates. This enables the MSD system to have ultra-sensitive detection limits, high specificity, large dynamic range, and low background signal. Another plate-based multiplex system is the SearchLight Plus CCD Imaging System produced by Aushon Biosystems. This novel multiplexing technology allows for the measurement of up to 16 different analytes simultaneously in a single microplate well. The assay design is similar to a sandwich ELISA where the capture antibodies are pre-spotted into individual wells of a 96-well plate. Samples or standards are added which bind to the specific capture antibodies and are detected using Aushon's patented SuperSignal ELISA Femto Chemiluminescent Substrate. Still another method is SomaLogic which is a bead-based technology for multiplex quantification of proteins or protein fragments.

The term “probe” refers to any molecule that is capable of selectively binding to a specifically intended target biomolecule, for example, a protein encoded by or corresponding to a biomarker. Probes can be synthesized by one of skill in the art, or derived from appropriate biological preparations. Probes may be specifically designed to be labeled. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules. Detectable labels include, but are not limited to, any heterologous addition to the probe that can be used to detect the selectively bound probe. Examples of detectable labels include fluorescent and radiological labels as well as labels that can be detected because they have a specific binding partner (ligand/receptor interaction) such as biotin/avidin or a nucleic acid tag that may act as a barcode to bind and specifically identify a larger nucleic acid.

As used herein the term “predicting progression” or “a prediction of progression” refers to providing a probability-based analysis of risk for osteoarthritis progression in a particular subject. The prediction of progression of osteoarthritis is not a guarantee or absolute, only a statistically probable indication of the disease state of the subject. The term prediction of a “diagnosis” or “diagnosing” of osteoarthritis refers to providing a probability-based analysis of an osteoarthritis diagnosis in a particular subject. The prediction of a diagnosis of osteoarthritis is not a guarantee or absolute, only a statistically probable indication of the disease state of the subject.

The level of the biomarker in the sample from the subject is compared to a reference level of the biomarker. The reference level may be determined empirically such as illustrated in the Examples, by comparison to the levels found in a set of samples from subjects with known clinical outcomes or known to have or not have osteoarthritis. Alternatively, the reference level may be a level of the biomarker found in samples, such as serum samples, which becomes a standard and can be used as a predictor for new samples. The level of the biomarker in the sample from the subject may be increased or decreased (i.e., “altered”) as compared to the reference level. The Examples and Tables provide information regarding how each biomarker is altered to indicate a diagnosis or to predict progression.

The predictive methods described herein may be combined to provide increased significance of the results, i.e. increased AUCs. For example, the levels of multiple markers may be determined in a sample from the subject and the results may have additional statistical or predictive power via the combination. The levels may be compared to the reference levels and a diagnosis or a prediction of risk of progression made. Several exemplary combinations are provided below and in the Examples, but any combination of 2, 3, 4, 5, 6, 7, 8, 9, 10 or more of the biomarkers may provide a prediction with increased accuracy and thus be beneficial.

Diagnosis

In some embodiments, the invention relates to a method of diagnosing osteoarthritis comprising measuring an expression level of at least one biomarker selected from the group consisting of CRAC1 (CRTAC1), A2AP, A1BG, A2GL, AACT, ACTG, AMBP, APOB, APOE, B2MG, C1QC, C1R, C1RL, C4BPA, C4BPB, CD14, CD44, CERU, CFAB, CFAH, CFAI, CILP1, C1S, CNDP1, CO2, CO4B, CO5, CO6A3, CO8B, CO8G, CO9, coll3, COMP, CTX1a, CTX1b, CTX2, CTXi, CXCL7, ECM1, FA12, FA5, FBLN1, FBLN3, FCGBP, FCN3, FETUA, FINC, GELS, HA, HABP2, haptoglobin, HEMO, HEP2, HGFA, HRG, hyaluronan, IC1, ITIH1, ITIH4, KNG1, LAMA2, LUM, LYAM1, MASP1, PCOC1, PGCA, PHLD, PLF4, PLMN, PRG4, RET4, SAMP, SHBG, TENX, TETN, THBG, TIMP1, TSP1, TSP4, VTDB, VTNC, ZA2G, ZPI, or any combination thereof in a sample from a subject; comparing the level of at least one biomarker in the sample to a reference level of the biomarker; and diagnosing the subject with osteoarthritis if the level of at least one biomarker is altered as compared to the reference level. In some embodiments, such methods further comprise developing a treatment plan for the subject if the subject is diagnosed with osteoarthritis.

In some embodiments, the biomarker is a combination of at least two of CRAC1, COMP, CO6A3, SHBG, PCOC1, CO8G, LUM, ACTG, CO5, A2AP, CO2, FA5, CERU, KNG1, HPLN1, CD14, CERU, CTX1a, CTX1b, VTNC, ZPI and haptoglobin and the diagnosis of osteoarthritis includes the presence of an osteophyte or a bone anabolic response. A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarker is a combination of at least one of CRAC1, CXCL7, C4BPA, COMP, LUM, CO5, TIMP1, C4BPA, PCOC1, A2AP, CO2, FA5, HRG, CO6A3, VTDB, KNG1, HPLN1, CD14, CERU, CTX1a, CTX1b, PLF4, TETN, TSP1, PHLD, C4BPB, CFAI, SAMP, CO8B, ECM1, TSP4, CILP, APOE, IHA, CTX2, CTXi, hyaluronan and haptoglobin and the diagnosis of osteoarthritis includes the presence of worsening joint space narrowing indicative of cartilage or meniscal abnormality. A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarker is a combination of at least one of CRAC1, SHBG, COMP, CO8G, PCOC1, CO6A3, LUM, CO5, A2AP, CO2, FA5, FBLN3, VTDB, KNG1, HPLN1, CD14, CERU, CTX1a, CTX1b, CTX2, CTXi, CFAH, TSP4 and haptoglobin and the diagnosis of osteoarthritis includes the progression of the Kellgren-Lawrence (KL) grade where progression is defined by a joint KL grade/score increasing to indicate a higher, i.e. worse, grade. A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarker includes CRAC1, KNG1 and/or haptoglobin for diagnosis of osteoarthritis.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), CXCL7, CO8G, ACTG, CD44, CERU, CFAH, CFAI, CO6A3, CO8G, COMP, FINC, HRG, KNG1, PLF4, PRG4, SAMP, TSP4, and any combination thereof. A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1) and CXCL7. In some embodiments, the biomarkers measured comprise CXCL7 and CO8G. In some embodiments, the biomarkers measured comprise CO8G and CRAC1. In still further embodiments, the biomarkers measured comprise CRAC1 (CRTAC1), CXCL7, and CO8G.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), A2AP, ACTG, APOE, C1QC, C4BPB, CD14, CFAI, CO5, CO6A3, CO8G, coll3, CXCL7, FA5, FCGBP, FINC, GELS, HA, HEMO, KNG1, PCOC1, TENX, VTDB, or any combination thereof. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), A2AP, ACTG, APOE, C1QC, C4BPB, CFAI, CO5, CO6A3, CO8G, CXCL7, FA5, FCGBP, FINC, GELS, PCOC1, TENX, or any combination thereof. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1), A2AP, ACTG, APOE, C1QC, C4BPB, CFAI, CO5, CO6A3, CO8G, CXCL7, FA5, FCGBP, FINC, GELS, PCOC1, and TENX. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CD14, coll3, HA, HEMO, KNG1, VTDB or any combination thereof. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), COMP, CO6A3, CO8G, CXCL7, or any combination thereof. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1), COMP, CO6A3, CO8G, and CXCL7.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), CXCL7, C4BPA, COMP, LUM, CO5, TIMP1, or any combination thereof. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1) and CXCL7. In further such embodiments, the biomarkers measured further comprise C4BPA and/or COMP. In still further such embodiments, the biomarkers measured further comprise LUM, CO5, and/or TIMP1.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), SHBG, COMP, CO8G, PCOC1, CO6A3, LUM, or any combination thereof. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1) and SHBG. In further such embodiments, the biomarkers measured further comprise COMP and/or CO8G. In still further such embodiments, the biomarkers measured further comprise PCOC1, CO6A3, and/or LUM. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1) and COMP. In further such embodiments, the biomarkers measured further comprise CO6A3 and/or SHBG.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), C4BPA, LUM, CO5, PCOC1, CXCL7, COMP, or any combination thereof. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1) and CXCL7. In further such embodiments, the biomarkers measured further comprise C4BPA and/or LUM. In still further such embodiments, the biomarkers measured further comprise CO5, PCOC1, and/or COMP.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), PCOC1, CO8G, LUM, COMP, CO6A3, CO5, ACTG or any combination thereof. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1) and PCOC1. In further such embodiments, the biomarkers measured further comprise COMP and/or CO8G. In still further such embodiments, the biomarkers measured further comprise LUM, CO6A3, and/or CO5. In some embodiments, the biomarkers measured comprise CRAC1 (CRTAC1) and COMP. In further such embodiments, the biomarkers measured further comprise CO6A3 and/or PCOC1. In still further such embodiments, the biomarkers measured comprise CO8G, ACTG, and/or CO5.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of A2AP, CO2, COMP, FA5, CO5, CRAC1 (CRTAC1), SHBG, or any combination thereof. In some embodiments, the biomarkers measured comprise A2AP and CO2. In further such embodiments, the biomarkers measured further comprise COMP and/or FA5. In still further such embodiments, the biomarkers measured further comprise CO5, CRAC1 (CRTAC1), and/or SHBG.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of A2AP, FA5, CO2, CO5, COMP, SHBG, CO6A3, or any combination thereof. In some embodiments, the biomarkers measured comprise A2AP and FA5. In further such embodiments, the biomarkers measured further comprise CO5 and/or CO2. In still further such embodiments, the biomarkers measured further comprise COMP, SHBG, and/or CO6A3. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of A2AP, CRAC1 (CRTAC1), CO2, COMP, FA5, CO5, or any combination thereof. In some embodiments, the biomarkers measured comprise A2AP and CO2. In further such embodiments, the biomarkers measured further comprise COMP and/or CRAC1 (CRTAC1). In some embodiments, the biomarkers measured comprise A2AP, CRAC1 (CRTAC1), and CO2. In further such embodiments, the biomarkers measured further comprise FA5. In still further such embodiments, the biomarkers measured further comprise COMP and/or CO5.

In some embodiments, the biomarkers measured comprise a peptide sequence listed in Table 2A or 2C. In some embodiments, the subject is diagnosed with osteoarthritis if the level of at least one biomarker is altered as shown in Table 2A or 2C.

Prognosis

In some embodiments, the invention relates to a method of predicting progression of osteoarthritis comprising measuring an expression level of at least one biomarker selected from the group consisting of A1BG, A2AP, A2GL, AACT, ACTG, AFAM, ANT3, APOB, APOH, B2MG, C1QC, C1R, C1RL, C4BPA, C4BPB, CD14, CD163, CD44, CERU, CFAB, CFAH, CFAI, C1S, CO2, CO4B, CO5, CO6A3, CO8B, coll3, CRAC1 (CRTAC1), CTX2, CXCL7, DOPO, ECM1, FA5, FA12, FBLN1, FCGBP, FCN3, FETUA, FINC, GELS, HABP2, haptoglobin, HEMO, HEP2, HGFA, HRG, hyaluronan, ITIH4, KLKB1, KNG1, LUM, LYAM1, PGCA, PHLD, PLF4, PLMN, PRG4, RET4, SAMP, TENX, TETN, THBG, THRB, TIMP1, TSP1, TSP4, VTDB, VTNC, or combinations thereof in a sample from a subject; comparing the level of the biomarker in the sample to a reference level of the biomarker; and predicting the progression of the osteoarthritis, wherein altered levels of any of the biomarkers as compared to the reference level is indicative of progression of the osteoarthritis. In some embodiments, such methods further comprise developing a treatment plan for the subject based on the prediction of progression of the osteoarthritis.

In some embodiments, the biomarker is a combination of at least two of PLF4, CXCL7, ANT3, AACT, THRB, ITIH4, CO8B, PLMN, PRG4, C4BPA, C4BPB, A2AP, LYAM1, CO8G, KLKB1, hyaluronan and haptoglobin and the prediction of osteoarthritis progression includes osteophyte growth or a bone anabolic response. A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarker is a combination of at least two of PGCA, APOH, SAMP, AACT, CFAH, PHLD, TSP1, THRB, HRG, CO4B, FCN3, CD44, TSP4, TETN, FINC, ECM1, HEMO, CD163, CERU, TIMP1, A1BG, THBG, A2GL, FBLN1, CO5, B2MG, FETUA and haptoglobin and the prediction of osteoarthritis progression includes worsening joint space narrowing. A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarker is a combination of at least one of CFAH, SAMP, TSP1, HEP2, C1R, APOB, FINC, PGCA, AACT, KNG1, A2AP, CO6A3, HGFA, CO2, PRG4, DOPO, CD44, CERU, VTDB, and haptoglobin and the prediction of osteoarthritis progression includes progression by the Kellgren-Lawrence (KL) grading scale (progression is defined as joint KL score increasing to a higher or worse grade). A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarkers include CO8B, haptoglobin and/or PLF4 and the prediction relates to the progression of osteoarthritis.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of ACTG, ANT3, CD44, CERU, CFAH, CFAI, CO8B, CXCL7, CO6A3, CRAC1 (CRTAC1), FINC, haptoglobin, HRG, KLKB1, PLF4, PRG4, SAMP, TSP4 and any combination thereof. A combination of all the listed biomarkers or only two, three, four, five, six, seven, eight, nine, ten or more may also be used. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of PLF4, CFAH, and ANT3. In still further embodiments, the biomarkers measured comprise PLF4, CFAH, and ANT3. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of ANT3, CD14, CD163, CD44, CERU, CFAH, CO8B, coll3, CRAC1 (CRTAC1), CTX2, CXCL7, haptoglobin, HEMO, HRG, KLKB1, LYAM1, VTDB, or any combination thereof. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of ANT3, CFAH, CO8B, CRAC1 (CRTAC1), CXCL7, HRG, KLKB1, LYAM1, or any combination thereof. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CFAH, CO8B, CRAC1 (CRTAC1), HRG, or any combination thereof. In some embodiments, the biomarkers measured comprise CFAH, CO8B, CRAC1 (CRTAC1), and HRG. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of ANT3, CO8B, CXCL7, KLKB1, LYAM1, or any combination thereof. In some embodiments, the biomarkers measured comprise ANT3, CO8B, CXCL7, KLKB1, and LYAM1. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from CD14, CD163, CD44, CERU, coll3, CTX2, haptoglobin, HEMO, VTDB or any combination thereof. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of ACTG, ANT3, CD44, CERU, CFAH, CFAI, CO8B, FINC, HRG, KLKB1, PLF4, PRG4, SAMP, TSP4 or any combination thereof. In some embodiments, the biomarkers measured comprise ACTG, ANT3, CD44, CERU, CFAH, CFAI, CO8B, FINC, HRG, KLKB1, PLF4, PRG4, SAMP, and TSP4.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of ANT3, CERU, CFAH, CO8B, FINC, HRG, PLF4, PRG4, SAMP, TSP4 or any combination thereof. In some embodiments, the biomarkers measured comprise ANT3, CERU, CFAH, CO8B, FINC, HRG, PLF4, PRG4, SAMP, and TSP4. In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of ACTG, ANT3, CD44, CFAI, CO8B, KLKB1, PLF4, or any combination thereof. In some embodiments, the biomarkers measured further comprise ACTG, ANT3, CD44, CFAI, CO8B, KLKB1, and PLF4.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of PGCA, APOH, SAMP, AACT, CFAH, PHLD, TSP1, THRB, or any combination thereof. In some embodiments, the biomarkers measured comprise SAMP and AACT. In further such embodiments, the biomarkers measured further comprise PGCA and/or APOH. In still further such embodiments, the biomarkers measured further comprise CFAH, PHLD, TSP1, and/or THRB. The prognosis includes joint space narrowing.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CFAH, SAMP, TSP1, HEP2, C1R, APOB, FINC, HEP2, PGCA, or any combination thereof. In some embodiments, the biomarkers measured comprise CFAH and SAMP. In further such embodiments, the biomarkers measured further comprise TSP1 and/or APOB. In still further such embodiments, the biomarkers measured further comprise HEP2, C1R, FINC, HEP2, and/or PGCA. The prediction includes KL grade.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of PLF4, CXCL7, ANT3, AACT, THRB, ITIH4, CO8B, PLMN, or any combination thereof. In some embodiments, the biomarkers measured comprise PLF4 and AACT. In further such embodiments, the biomarkers measured further comprise CXCL7 and/or ANT3. In still further such embodiments, the biomarkers measured further comprise THRB, ITIH4, CO8B, and/or PLMN. The prediction includes osteophyte growth.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of TSP1, CFAH, THRB, HRG, APOH, AACT, PHLD, AACT, or any combination thereof. In some embodiments, the biomarkers measured comprise TSP1 and CFAH. In further such embodiments, the biomarkers measured further comprise THRB and/or APOH. In still further such embodiments, the biomarkers measured further comprise HRG, AACT, PHLD, and/or AACT. The prediction includes joint space narrowing.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CFAH, TSP1, SAMP, APOB, AACT, C1R, or any combination thereof. In some embodiments, the biomarkers measured comprise CFAH and SAMP. In further such embodiments, the biomarkers measured further comprise TSP1 and/or AACT. In still further such embodiments, the biomarkers measured further comprise APOB and/or C1R. The prediction includes the KL grade.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CO8B, PLF4, PRG4, ANT3, C4BPA, CXCL7, C4BPA, or any combination thereof. In some embodiments, the biomarkers measured comprise CO8B and PLF4. In further such embodiments, the biomarkers measured further comprise PRG4 and/or ANT3. In still further such embodiments, the biomarkers measured further comprise C4BPA, CXCL7, and/or C4BPA. The prediction may include osteophyte growth.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CO4B, FCN3, CO8B, FINC, PGCA, TSP4, TETN, or any combination thereof. In some embodiments, the biomarkers measured comprise CO8B and CO4B. In further such embodiments, the biomarkers measured further comprise FINC and/or FCN3. In still further such embodiments, the biomarkers measured further comprise PGCA, TSP4, and/or TETN. The prognosis may include joint space narrowing.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of A2AP, KNG1, HGFA, PRG4, AFAM, DOPO, FINC, CO8B, or any combination thereof. In some embodiments, the biomarkers measured comprise KNG1 and HGFA. In further such embodiments, the biomarkers measured further comprise A2AP and/or CO8B. In still further such embodiments, the biomarkers measured further comprise PRG4, AFAM, DOPO, and/or FINC. The prediction includes a KL grade determination.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of CXCL7, C4BPA, C4BPB, A2AP, ITIH4, PLMN, HRG, or any combination thereof. In some embodiments, the biomarkers measured comprise CXCL7 and C4BPA. In further such embodiments, the biomarkers measured further comprise C4BPB and/or A2AP. In still further such embodiments, the biomarkers measured further comprise ITIH4, PLMN, and/or HRG. The prediction includes osteophyte growth.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of PGCA, CO4B, TENX, FCN3, C4BPA, TSP1, CO8B, HRG, or any combination thereof. In some embodiments, the biomarkers measured comprise CO4B and PGCA. In further such embodiments, the biomarkers measured further comprise TENX and/or C4BPA. In still further such embodiments, the biomarkers measured further comprise FCN3, TSP1, CO8B, and/or HRG. The prognosis may include joint space narrowing.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of KNG1, HGFA, A2AP, FA5, TSP1, PGCA, TENX, FINC, or any combination thereof. In some embodiments, the biomarkers measured comprise KNG1 and HGFA. In further such embodiments, the biomarkers measured further comprise A2AP and/or PGCA. In still further such embodiments, the biomarkers measured further comprise FA5, TSP1, TENX, and/or FINC. The prediction includes KL grade prediction.

In some embodiments, the biomarkers measured comprise at least two biomarkers selected from the group consisting of C4BPA, C4BPB, CXCL7, LYAM1, A2AP, TSP1, FINC, or any combination thereof. In some embodiments, the biomarkers measured comprise C4BPA and CXCL7. In further such embodiments, the biomarkers measured further comprise C4BPB and/or LYAM1. In still further such embodiments, the biomarkers measured further comprise A2AP, TSP1, and/or FINC. The prediction includes osteophyte growth.

In some embodiments, the biomarkers measured comprise a peptide sequence listed in Table 2A, 2C or 2D. In some embodiments, a prediction of progression of the osteoarthritis in the subject is made if the level of at least one biomarker is altered as compared to the reference level as shown in Table 2A or 2D. In some embodiments, a diagnosis of osteoarthritis or a prediction of progression of the osteoarthritis in the subject is made if the level of at least one biomarker is greater or less than the threshold values shown in Table 2A or 2C. In some embodiments, the biomarkers comprise the sequences listed in the Tables included herein. The various embodiments described herein may be combined or used individually.

The present disclosure is not limited to the specific details of construction, arrangement of components, or method steps set forth herein. The compositions and methods disclosed herein are capable of being made, practiced, used, carried out and/or formed in various ways that will be apparent to one of skill in the art in light of the disclosure that follows. The phraseology and terminology used herein is for the purpose of description only and should not be regarded as limiting to the scope of the claims. Ordinal indicators, such as first, second, and third, as used in the description and the claims to refer to various structures or method steps, are not meant to be construed to indicate any specific structures or steps, or any particular order or configuration to such structures or steps. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to facilitate the disclosure and does not imply any limitation on the scope of the disclosure unless otherwise claimed. No language in the specification, and no structures shown in the drawings, should be construed as indicating that any non-claimed element is essential to the practice of the disclosed subject matter. The use herein of the terms “including,” “comprising,” or “having,” and variations thereof, is meant to encompass the elements listed thereafter and equivalents thereof, as well as additional elements. Embodiments recited as “including,” “comprising,” or “having” certain elements are also contemplated as “consisting essentially of” and “consisting of” those certain elements.

Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. For example, if a concentration range is stated as 1% to 50%, it is intended that values such as 2% to 40%, 10% to 30%, or 1% to 3%, etc., are expressly enumerated in this specification. As another example, if it is stated that the biomarkers measured comprise “at least one (or any other number)” biomarker selected from a particular group it is intended that values such as “at least two,” “at least three,” “at least four,” etc. (up until the maximum allowed by the statement) are expressly enumerated in the specification. These are only examples of what is specifically intended, and all possible combinations of numerical values between and including the lowest value and the highest value enumerated are to be considered to be expressly stated in this disclosure. Use of the word “about” to describe a particular recited amount or range of amounts is meant to indicate that values very near to the recited amount are included in that amount, such as values that could or naturally would be accounted for due to manufacturing tolerances, instrument and human error in forming measurements, and the like. All percentages referring to amounts are by weight unless indicated otherwise.

No admission is made that any reference, including any non-patent or patent document cited in this specification, constitutes prior art. In particular, it will be understood that, unless otherwise stated, reference to any document herein does not constitute an admission that any of these documents forms part of the common general knowledge in the art in the United States or in any other country. Any discussion of the references states what their authors assert, and the applicant reserves the right to challenge the accuracy and pertinence of any of the documents cited herein. All references cited herein are fully incorporated by reference, unless explicitly indicated otherwise. The present disclosure shall control in the event there are any disparities between any definitions and/or description found in the cited references.

The following examples are meant only to be illustrative and are not meant as limitations on the scope of the invention or of the appended claims.

Examples

For this project, candidate prognostic and diagnostic biomarkers in non-depleted (normal serum) and depleted serum (serum treated to deplete one or more abundant serum proteins as described more fully below) were evaluated by mass spectrometry. We tested a multiple reaction monitoring (MRM) panel developed on the basis of three discovery proteomics experiments: in synovial fluid, urine and depleted serum. Below we describe the results for a cohort of 124; approximately two-thirds of the subjects were selected on the basis of knee osteoarthritis with either knee OA progression or stability (non-progression) and the remaining one-third of subjects were controls without knee osteoarthritis.

Subjects and Methods

Cohorts:

Subjects were selected from two cohorts, the Prediction of Osteoarthritis

Progression (POP) cohort, and the Genetics of Generalized Osteoarthritis (GOGO) cohort. Kraus et al., Ann Rheum Dis 68(11):1673-9 (2009); Kraus et al., Osteoarthritis Cartilage 15(2):120-7 (2007). In brief, POP was a single site study at Duke with 159 subjects recruited on the basis of symptomatic radiographic knee OA of at least one knee; a total of 138 subjects (87%) returned for 3-year follow-up. Knee synovial fluid (from both knees when possible), serum (2 hour post-prandial) and urine (second morning void) were obtained at each evaluation. GOGO was a multi-site (7 sites) study with 2728 subjects recruited on the basis of two siblings with hand OA (defined as 3 joint radiographic KL ≥2 grade distributed bilaterally). Knee, hip and spine radiographs were obtained. A total of 1329 subjects from 4 sites returned for follow-up at a mean 3.8 years (range 1.4-6.5 years). Serum (two hour post-prandial) and urine (second morning void or time recorded if other than this) were obtained at each evaluation. The demographics are shown in Table 1 for the patient subsets used for each aspect of biomarker discovery and validation.

Phenotypes

Phenotypes were based on 2 features from knee radiographs (joint space narrowing (JSN) and osteophyte (OST)) evaluated at 2 levels (knee based and person-based). JSN, indicative of cartilage and/or meniscal extrusion loss (cartilage and/or meniscal catabolism), and osteophyte, indicative of bone formation at the margins of the joint (joint tissue anabolism), were graded on a scale 0-3 using a standardized atlas with 0 being normal and 1, 2, and 3 representing increasing severity. Altman R D and Gold G E, Osteoarthritis Cartilage 15 Suppl A:A1-56 (2007). The medial and lateral compartments of the knee were graded for JSN (yielding a maximum score of 6 per knee joint); the four margins of the knee were graded for OST (yielding a maximum score of 12 per knee joint). Progression was defined as a one unit change over time in these variables at a knee level or person level. A Diagnosis of OA was defined as any OA represented by a score of greater than or equal to 1 for these variables at a knee level or person level. In addition, a half-century old definition of OA, the Kellgren Lawrence grade, scored on a 0-4 scale, was also evaluated for a one unit change (defining progression) or any OA (defined as KL ≥2). Kellgren J H and Lawrence J S, Ann Rheum Dis 16(4):494-502 (1957). Controls for each phenotype were defined as a knee or person that did not meet the minimal definition.

TABLE 1 Patient demographics for sample sets used in experiments. C = control Number for JSN Number for OST Mean Age, Mean BMI, NP = non-progressor Person knee Person knee SD (range) Gender SD (range) P = progressor C/NP/P C/NP/P years % female kg/m² SF Proteomics 1/12/10 3/5/15 67 ± 12 74% 30.4 ± 5.8 (N = 23) 9/16/21 9/16/21 (43-80) (23-47) Urine 14/16/15 16/9/20 62 ± 12 76% 29.3 ± 6.6 Proteomics 32/36/22 37/23/30 (41 to 81) (18.6-50.0) (N = 45) Urine ELISA 47/32/33 40/24/48 65 ± 10 59% 28.7 ± 6.6 (N = 118) 109/65/52 94/66/66 (41-86) (18.6-61.7) Serum MRM 4/16/20 3/11/26 63 ± 12 78% 30.0 ± 6.1 Validation 13/38/29 10/30/40 (42-80) (20-50) (N = 40) Serum MRM 50/34/38 41/37/54 64 ± 10 82% 27.6 ± 6.1 and ELISA 116/69/61 98/72/76 (41-86) (18.6-61.7) (N = 124) C = control' NP = non-progressor; P = progressor; SF = synovial fluid; MRM = multiple reaction monitoring

Statistical Methods

Two classes of methods were used, inferential methods and predictive methods, depending on the structure of the data. For person-level analysis, logistic regression was used to compute p-values and confidence intervals. Covariates included age, sex, BMI, and cohort. The effect of a biomarker was added to a model containing these covariates and a likelihood-ratio test was used to assess the significance of the biomarker after accounting for the covariates. A biomarker was considered significant if it surpassed a Benjamini-Hochberg FDR threshold of 10%. We defined person level phenotypes as follows: a person-level control as both knees normal; a person level osteoarthritis diagnosis as at least one knee with osteoarthritis; a person level knee osteoarthritis progressor as having at least one knee that has progressive osteoarthritis over time; and a person level non-progressor as having neither knee progressing over time. For a knee-level analysis the dependence arising from paired observations must be considered. We used the generalized estimating equation (GEE) method to account for the correlation structure. A biomarker was added to a model containing base covariates and its significance was assessed by a Wald statistic.

We evaluated the capability of the biomarkers described herein to predict or diagnose osteoarthritis based on its separate features consisting of: cartilage and/or meniscal extrusion or loss (reflected in radiographic joint space narrowing), an anabolic repair response (reflected in radiographic osteophyte presence and growth), and the Kellgren-Lawrence grade of disease (reflecting both osteophyte and joint space narrowing).

Predictive models were used to assess discrimination through the AUC. We used feature selection coupled with ridge regression, a form of penalized regression, for all models implemented in the glmnet R package. Penalized regression is often used for predictive models to constrain the size of coefficients to lessen the effects of overfitting the data. Feature selection consisted of selecting the 8 markers with the lowest p-values, which is a simple but effective method for the numbers of peptides in the current data set. Leave-one-out cross-validation was used in which selecting tuning parameters and carrying out feature selection was repeated at each iteration of cross-validation to mimic the process of fitting a model to new data. In sum, all multimarker AUCs have been properly cross-validated. For knee-level (paired) analysis, the leave-one-out cross-validation scheme was modified to a leave-sample-out scheme so that the test set was independent of the training set. Of note, an AUC 0.80≈sensitivity and specificity of 75% (depending on clinical context) and odds ratio 9-10. Qoronfleh et al., Drug Discovery World Winter:19-28 (2011).

The mean and SD values of ELISA results are natural log values for two of the serum markers that had skewed distributions (Hyaluronan and Ceruloplasmin), and all the urine biomarkers. None of the other serum markers were log transformed. Some values are negative because they are natural log transformations of values less than 1. In particular, all of the urine values were normalized prior to the analysis. Two separate ways of normalizing the urine values were tested: 1) by dividing the urine biomarker level by the creatinine value and taking the natural log [urine 1 variation], and 2) by dividing the urine biomarker level by the creatinine level and the cystatin level and taking the natural log [urine 2 variation]. In this case it is possible for some values to be negative because the natural log of a fraction is <0.

Of note, for the MRM analyses, the same amount of heavy labeled peptide was spiked into depleted and non-depleted serum; in retrospect, approximately one third this amount would have been more appropriate for non-depleted serum as it would have more closely approximated amounts of the biomarkers targeted for quantification in the non-depleted serum.

Results 1. Technical Validation

To assess analytical variation across all of the targeted analytes, a cost effective strategy for quality control (QC) was adopted a priori for this project. This consisted of creating a representative quality control sample by pooling equal portions of all patient samples within a particular matrix (i.e. synovial fluid, urine or serum). This approach allows for any matrix-specific interferences to be included in the technical variation calculations.

The analytical measurements were made from this QC sample many times throughout the analysis of the cohort samples. This allowed the measurement of the reproducibility of the quantification for the exact species of interest at the concentration levels where they are found in the sample. The closer an absolute signal is to its limit of detection or lower limit of quantification, the lower the expected reproducibility (or greater the expected variability). For this study we defined the acceptable upper limit of variability of repeated measurements of any analyte within a sample to be 30% relative to the standard deviation. This limit was based on our expectation that any real biological or treatment group dependent variation would achieve this level of variability or higher; this threshold is consistent with a range of coefficients of variation (CVs 20-35%) for proteomics studies deemed acceptable in a recent summary of a workshop held at the National Institutes of Health with representatives from the multiple communities developing and employing targeted mass spectrometry assays. Carr et al., Mol Cell Proteomics 13(3):907-17 (2014). FIG. 2 (above) plots CVs for each of the 147 targeted analytes representing 99 proteins (as a ratio of the heavy to light peptides) across all analyses of the QC sample; of note this does not include the 6 Alcohol Dehydrogenase control peptides. The blue dots represent peptides accepted into the final MRM panel of 146 analytes; the red dots represent the peptides rejected from the final dataset for analysis. As expected, the CVs (blue dots) tend to increase at lower ratios because their quantity approaches the lower limit of quantification for these analytes. The red dots show no correlation to ratio, as their quantification is likely confounded by matrix interferences.

2. Proteomics Results

For this project, candidate prognostic and diagnostic biomarkers were identified by three means: through discovery proteomics experiments in synovial fluid and urine by liquid chromatography mass spectrometry (LC/MS); and a discovery proteomics experiment in depleted serum (serum depleted of the 14 most abundant serum proteins. Based on this work, we selected a potential list of 155 peptides corresponding to 110 proteins for MRM assay development. The MRM assay was evaluated in synovial fluid (pooled samples from 3 progressor and 3 non-progressor knee OA patients) and test sera (3 progressor and 3 non-progressor knee OA patient samples for both depleted and non-depleted serum). Of the original 155 peptides, a total of 146 peptides from 99 proteins were detectable in these test samples and selected for final validation in depleted and non-depleted serum of 124 patients by MRM. Additionally, ELISA based analyses were used to evaluate some prognostic and diagnostic biomarker candidates when commercial ELISA kits were available for a biomarker of interest.

As described above, a final total of 146 peptides (99 proteins) were evaluated in this study by mass spectrometry. Below are listed the results from serum proteomic analysis of the non-depleted serum and the MARS14 depleted serum. MARS14 depleted serum is serum after it has been subjected to a column absorption to remove 14 of the most abundant serum proteins such that the abundance of these proteins does not interfere with the measurement and analysis of other serum proteins. The 14 depleted proteins include the following: albumin, IgG, IgA, transferrin, haptoglobin, anti-trypsin, fibrinogen, alpha 2-macroglobulin, IgM, apolipoprotein AI, apolipoprotein AII, complement C3, and transthyretin. To date, more extensive statistical analyses have been performed on the non-depleted serum because results were stronger than with the depleted serum; in general results are consistent between the two. We also experimented with the use of a normalization peptide (actin) in one case (diagnosis of knee level Osteoarthritis) and it showed a slight improvement in AUCs. Further normalization peptides are reported below. A total of 19 of 97 of the panel of OA proteins were predicted to be involved in the pathogenesis of OA and might have the potential to be ‘direct biomarkers’ of OA; 1 of these 19 was associated with development of a connective tissue disorder; and 16 of the 19 were linked to the process of post-translational modification, protein degradation and synthesis in OA cartilage. A summary of the markers and corresponding peptides for the proteomic results discussed below is shown in Tables 2A, 2B, 2C and 2D.

TABLE 2A Summary of Markers and Corresponding Peptides; lower case marker names in peptide sequence column indicate markers that were detected by ELISA. All markers listed were found to have a statistically significant association (p- value < 0.05) with osteoarthritis diagnosis, prediction of osteoarthritis progression, or both. Diagnos Prognos SEQ (/ug (/ug Acces- ID total total sion Peptide NO: pro- pro- Marker No. Sequence(s) X tein) tein) CRAC1 Q9NQ79 GVASLFAGR   1 > > (CRTAC1) SSPYYALR   2 > > A1BG P04217 IFFHLNAVALG   3 > DGGHYTCR A2AP P08697 SPPGVCSR   4 > LCQDLGPGAFR   5 < A2GL P02750 VAAGAFQGLR   6 > ALGHLDLSGNR   7 > AACT P01011 ADLSGITGAR   8 > NLAVSQVVHK   9 > EQLSLLDR  10 > ACTG P63261 VAPEEHPVLLT   11 < EA PLNPK AFAM P43652 VNCLQTR  12 AMBP P02760 AFIQLWAFDAV  13 K ANT3 P01008 ATEDEGSEQK  14 < APOB P04114 LAIPEGK  15 < > WNFYYSPQSSP  16 DK APOE P02649 LQAEAFQAR  17 > APOH P02749 ATFGCHDGYSL  18 > DGPEEIECTK B2MG P61769 VEHSDLSFSK  19 C1QC P02747 VVTFCGHTSK  20 <,> C1R P00736 NIGEFCGK  21 GLTLHLK  22 > GYGFYTK  23 > C1RL Q9NZP8 GSEAINAPGDN  24 PAK C4BPA P04003 LSLEIEQLELQ  25 > R GVGWSHPLPQC  26 > > EIVK C4BPB P20851 SQCLEDHTWAP  27 > PF PICK CD14 cd14 CD163 cd163 CD44 P16070 YGFIEGHVVIP  28 > R cd44 CERU P00450 HYYIAAEEIIW  29 < NY APSGIDIFTK cerulo EYTDASFTNR  30 DIASGLIGPLI  31 ICK GAYPLSIEPIG  32 VR EVGPTNADPVC  33 LAK CFAB P00751 QLNEINYEDHK  34 CFAH P08603 CLPVTAPENGK  35 > CFAI P05156 HGNTDSEGIVE  36 > > VK AQLGDLPWQVA  37 > IK CILP1 O75339 IVGPLEVNVR  38 C1S P09871 LLEVPEGR  39 > CNDP1 Q96KN2 ALEQDLPVNIK  40 CO2 P06681 SSGQWQTPGAT  41 < R DGNDHSLWR  42 > CO4B P0C0L5 LVNGQSHISLS  43 K CO5 P01031 GIYGTISR  44 > > TLLPVSKPEIR  45 > > IIHFGTR  46 > FSYSSGHVHLS  47 SENK SYFPESWLWEV   48 HL VPR CO6A3 P12111 EVQVFEITENS  49 > AK LLPSFVSSENA  50 FYLS PDIR CO8B P07358 GILNEIK  51 > > CO8G P07360 QLYGDTGVLGR  52 > > CO9 P02748 FTPTETNK  53 coll3 coll3 COMP P49747 NALWHTGDTES  54 > QVR SSTGPGEQLR  55 > SNPDQADVDHD  56 FVGDAC DSDQDQDGDGH QDSR CTX1a ctx1a_ctx1b CTX1b ctx1a_ctx1b CTX2 ctx2 CTXi ctxi CXCL7 P02775 NIQSLEVIGK  57 > > DOPO P09172 VISTLEEPTPQ  58 CPT SQGR ECM1 Q16610 FCEAEFSVK  59 > FA12 P00748 CLEVEGHR  60 FA5 P12259 SEAYNTFSER  61 > EFNPLVIVGLS  62 > K FBLN1 P23142 TGYYFDGISR  63 FBLN3 Q12805 NPCQDPYILTP  64 > ENR ADQVCINLR  65 > FCGBP Q9Y6R7 VTASSPVAVLS   66 GH SCAQK FCN3 O75636 TFAHYATFR  67 FETUA P02765 HTLNQIDEVK  68 FSVVYAK  69 > FINC P02751 EYLGAICSCTC   70 > F GGQR IGDTWSK  71 > GELS P06396 GGVASGFK  72 > HA HA HABP2 Q14520 FCEIGSDDCYV   73 G DGYSYR GQCLITQSPPY  74 > YR hapto- Hapto globin HEMO P02790 QGHNSVFLIK  75 hemopexin HEP2 P05546 NFGYTLR  76 > FTVDRPFLFLI  77 > YEHR HGFA Q04756 YIPYTLYSVFN   78 PS DHDLVLIR HRG P04196 DSPVLIDFFED  79 > TER GGEGTGYFVDF  80 SVR YWNDCEPPDSR  81 > GEVLPLPEANF   82 PS FPLPHHK SSTTKPPFKPH  83 GSR hyaluronan hyaluronan IC1 P05155 LVLLNAIYLSA  84 K ITIH1 P19827 VTFQLTYEEVL  85 K ITIH4 Q14624 FKPTLSQQQK  86 > KLKB1 P03952 VSEGNHDIALI  87 > K KNG1 P01042 LDDDLEHQGGH   88 > VLDHGHK kinno LAMA2 P24043 TPYNILSSPDY  89 VGVTK LUM P51884 ILGPLSYSK  90 > VANEVTLN  91 > SLEDLQLTHNK  92 LYAM1 P14151 AEIEYLEK  93 MASP1 P48740 TGVITSPDFPN  94 PYPK PCOC1 Q15113 TGGLDLPSPPT  95 > GASLK PGCA P16112 VSLPNYPAIPS   96 < > D ATLEVQSLR EVVLLVATEGR  97 PHLD P80108 FGSSLITVR  98 > > PLF4 P02776 ICLDLQAPLYK  99 > > PLMN P00747 HSIFTPETNPR 100 > PRG4 Q92954 ITEVWGIPSPI 101 > DTVFTR DQYYNIDVPSR 102 > RET4 P02753 LIVHNGYCDGR 103 > SAMP P02743 AYSDLSR 104 > > SHBG P04278 IALGGLLFPAS 105 < NLR TENX P22105 TVTVEDLEPGK 106 TETN P05452 TFHEASEDCIS 107 > R THBG P05543 NALALFVLPK 108 AVLHIGEK 109 THRB P00734 NPDSSTTGPWC  110 > YTTDPTVR TIMP1 P01033 GFQALGDAADI 111 > > R TSP1 P07996 FVFGTTPEDIL 112 > > R TSP4 P35443 DVDIDSYPDEE  113 > L PCSAR AVAEPGIQLK 114 VTDB P02774 vitd_ binding ELPEHTVK 115 VLEPTLK 116 LCDNLSTK 117 SCESNSPFPVH  118 PG TAECCTK SLGECCDVEDS  119 T TCFNAK VTNC P04004 QPQFISR 120 < ZA2G P25311 DIVEYYNDSNG  121 SHVLQGR ZPI Q9UK55 VVNPTLL 122 >

TABLE 2B Peptide key Protein name Biology A1BG P04217 Alpha-1B-glycoprotein Plasma protein A2AP P08697 Alpha-2-antiplasmin Acute phase serine protease inhibitor A2GL P02750 Leucine-rich alpha-2- Plasma protein glycoprotein AACT P01011 Alpha-1-antichymotrypsin Acute phase serine protease inhibitor ACTG P63261 Actin, cytoplasmic 2 Cytoskeleton component AFAM P43652 Afamin Vitamin E binding protein AMBP P02760 Protein AMBP Inter-alpha-trypsin inhibitor (a serpin) ANGT P01019 Angiotensinogen regulator of blood pressure, body fluid and electrolyte homeostasis ANT3 P01008 Antithrombin-III serine protease inhibitor in blood coagulation APOB P04114 Apolipoprotein B-100 major protein constituent of chylomicrons, LDL and VLDL APOE P02649 Apolipoprotein E binding, internalization, and catabolism of lipoprotein particles APOH P02749 Beta-2-glycoprotein 1 Heparin sulphate binding plasma protein B2MG P61769 Beta-2-microglobulin Component of the class I MHC BTD P43251 Biotinidase release of biotin from biocytin C1QC P02747 Complement C1q Complement pathway subcomponent subunit C C1R P00736 Complement C1r Complement pathway subcomponent C1RL Q9NZP8 Complement C1r Mediates the proteolytic cleavage of subcomponent-like protein HP/haptoglobin in the ER C1S P09871 Complement C1s Complement pathway subcomponent C4BPA P04003 C4b-binding protein alpha Complement pathway chain C4BPB P20851 C4b-binding protein beta Complement pathway chain CD44 P16070 CD44 HA cell surface binding receptor CERU P00450 Ceruloplasmin ferroxidase activity and iron transport across the cell membrane CFAB P00751 Complement factor B Complement pathway CFAH P08603 Complement factor H Complement pathway CFAI P05156 Complement factor I Complement pathway CILP1 O75339 Cartilage intermediate Cartilage protein layer protein 1 CNDP1 Q96KN2 Beta-Ala-His dipeptidase Serum metaloproteinase CO2 P06681 Complement C2 Complement pathway CO4B P0C0L5 Complement C4-B Complement pathway CO5 P01031 Complement C5 Complement pathway CO5A1 P20908 Collagen alpha-1(V) chain Type V fibular collagen CO5A1 P20908 Complement component Complement pathway C6 CO6 P13671 Complement component Complement pathway C6 CO6A3 P12111 Collagen alpha-3(VI) chain Collagen VI, cell binding CO8B P07358 Complement component Complement pathway C8 beta chain CO8G P07360 Complement component Complement pathway C8 gamma chain CO9 P02748 Complement component Complement pathway C9 CO9A3 Q14050 Collagen alpha-3(IX) chain Type IX collagen COMP P49747 Cartilage oligomatrix Cartilage ECM protein protein COOA1 Q17RW2 Collagen alpha-1(XXIV) Fibular collagen XIV chain CRAC1 Q9NQ79 Cartilage acidic protein 1 ECM protein found in cartilage, bone CRTAC and lung CSPG2 P13611 Versican core protein ECM protein binds HA CXCL7 P02775 Platelet basic protein stimulates DNA synthesis, mitosis, glycolysis, cAMP accumulation, prostaglandin E2, and synthesis of HA and GAG DOPO P09172 Dopamine beta- Conversion of dopamine to hydroxylase noradrenaline ECM1 Q16610 Extracellular matrix Involved in endochondral bone protein 1 formation as negative regulator of bone mineralization F13B P05160 Coagulation factor XIII B Glycoprotein involved in blood chain coagulation FA12 P00748 Coagulation factor XII Serine proteinase involved in blood coagulation FA5 P12259 Coagulation factor V Blood coagulation, Hemostasis FBLN1 P23142 Fibulin-1 ECM protein FBLN3 Q12805 EGF-containing fibulin- Fibulin-3, ECM protein, May function like extracellular matrix as a negative regulator of chondrocyte protein 1 differentiation FCGBP Q9Y6R7 IgGFc-binding protein May be involved in the maintenance of the mucosal structure FCN3 O75636 Ficolin-3 May function in innate immunity FETUA P02765 Alpha-2-HS-glycoprotein influences the mineral phase of bone FINC P02751 Fibronectin ECM multiple roles FREM2 Q5SZK8 FRAS1-related Cell membrane adhesion protein extracellular matrix protein 2 GELS P06396 Gelsolin Plasma protein HABP2 Q14520 Hyaluronan-binding Plasma serine proteinase protein 2 HEMO P02790 Hemopexin Binds heme and transports it to the liver HEP2 P05546 Heparin cofactor 2 Blood coagulation, Chemotaxis, hemostatsis HGFA Q04756 Hepatocyte growth factor Activates hepatocyte growth factor activator HPLN1 P10915 Hyaluronan and Link protein, cartilage ECM HA proteoglycan link protein 1 binding protein HRG P04196 Histidine-rich glycoprotein Angiogenesis, Blood coagulation, Chemotaxis, Fibrinolysis, Hemostasis IC1 P05155 Plasma protease C1 SERPING1 inhibits C1 of complement inhibitor IRK4 P48050 Inward rectifier potassium Potassium channel channel 4 ITIH1 P19827 Inter-alpha-trypsin Protease inhibitor, Serine protease inhibitor heavy chain H1 inhibitor, binds HA ITIH4 Q14624 Inter-alpha-trypsin Acute phase protein inhibitor heavy chain H4 KIZ Q2M2Z5 Centrosomal protein Centrosomal protein kizuna KLKB1 P03952 Plasma kallikrein Serine protease KNG1 P01042 Kininogen-1 Protease inhibitor, Thiol protease inhibitor, Vasoactive, Vasodilator LAMA2 P24043 Laminin subunit alpha-2 Cell ECM binding LUM P51884 Lumican Cartilage ECM protein LYAM1 P14151 L-selectin rolling of leukocytes MASP1 P48740 Mannan-binding lectin Lectin complement pathway serine protease 1 MAST3 O60307 Microtubule-associated Cytoplasmic enzyme serine/threonine-protein kinase 3 PCOC1 Q15113 Procollagen C- Type 1 collagen fibril formation endopeptidase enhancer 1 PGBM P98160 Perlecan ECM protein PGCA P16112 Aggrecan core protein Cartilage ECM HA binding protein PGRP2 Q96PD5 N-acetylmuramoyl-L- May play a scavenger role by digesting alanine amidase biologically active peptidoglycan PHLD P80108 Phosphatidylinositol- hydrolyzes the inositol phosphate glycan-specific linkage in proteins phospholipase D PLF4 P02776 Platelet factor 4 Released during platelet aggregation PLMN P00747 Plasminogen Blood coagulation, Fibrinolysis, Hemostasis. Tissue remodeling PRG4 Q92954 Proteoglycan 4 Lubricin, cartilage ECM protein PRLD2 Q8N945 PRELI domain containing Mitrochondrial 2 PZP P20742 Pregnancy zone protein inhibit all four classes of proteinases RET4 P02753 Retinol-binding protein 4 Retinol transporter protein RTN4 Q9NQC3 Reticulon-4 neurogenesis RUNX2 Q13950 Runt-related transcription osteoblastic differentiation and skeletal factor 2 morphogenesis SAMP P02743 Serum amyloid P- Can interact with DNA and histones component and may scavenge nuclear material released from damaged circulating cells SEPP1 P49908 Selenoprotein P Selenium transport and ECM antioxidant SHBG P04278 Sex hormone-binding androgen transport protein globulin SPTA2 Q13813 Spectrin alpha chain, non- Ca depended cytoskeletal re- erythrocytic 1 organization TENX P22105 Tenascin X Anti-adhesive ECM glycoprotein TETN P05452 Tetranectin Plasma protein THBG P05543 Thyroxine-binding Major thyroid hormone transport globulin protein in serum. THRB P00734 Prothrombin Acute phase, Blood coagulation, Hemostasis TIMP1 P01033 Tissue inhibitor MMP inhibitor metalloproteinase 1 TNR6C Q9HCJ0 trinucleotide repeat miRNA silencing containing 6C TSP1 P07996 Thrombospondin 1 Cell/cell/matrix binding TSP4 P35443 Thrombospondin-4 Mediates cell/cell and cell/matrix adhesion VTDB P02774 Vitamin D-binding protein Vitamin D transporter protein VTNC P04004 vitronectin Cell/matrix adhesion factor ZA2G P25311 Zinc-alpha-2-glycoprotein Stimulates lipid degradation in adipocytes ZPI Q9UK55 Protein Z-dependent Blood coagulation, Hemostasis protease inhibitor

TABLE 2C Diagnostic Data (OA = Osteoarthritis, C = Control) Diagnostic Data phenotype (SEQ ID NO: X) OAmean OAsd Cmean Csd MRM markers; non-depleted Serum; knee Knee_JSN_Pheno_Q9NQ79_CRAC1_GVA −5.3467 0.31328 −5.5677 0.29133 SLFAGR (1) Knee_JSN_Pheno_P02775_CXCL7_NIQSL −1.579 0.26191 −1.8924 0.78391 EVIGK (57) Knee_JSN_Pheno_P04217_A1BG_IFFHLN 0.01795 0.2559 −0.0399 0.22048 AVALGDGGHYTCR (3) Knee_JSN_Pheno_P02776_PLF4_ICLDLQ −2.6781 0.31999 −2.9236 0.74256 APLYK (99) Knee_JSN_Pheno_P05452_TETN_TFHEAS −2.7375 0.23026 −2.824 0.23499 EDCISR (107) Knee_JSN_Pheno_P01033_TIMP1_GFQAL −5.5478 0.39529 −5.7389 0.421 GDAADIR (111) Knee_JSN_Pheno_P49747_COMP_NALW −5.2222 0.39632 −5.4563 0.48604 HTGDTESQVR (54) Knee_JSN_Pheno_P04003_C4BPA_LSLEI −0.6158 0.24333 −0.732 0.21636 EQLELQR (25) Knee_JSN_Pheno_P07996_TSP1_FVFGTT −3.9111 0.33405 −4.1246 0.70016 PEDILR (112) Knee_JSN_Pheno_P80108_PHLD_FGS_S_LI −3.9109 0.31233 −4.0202 0.295 TVR (98) Knee_JSN_Pheno_P16112_PGCA_VSLPN −8.5077 0.60466 −8.3027 0.59736 YPAIPSDATLEVQSLR (96) Knee_JSN_Pheno_P04003_C4BPA_GVGW −0.6333 0.30499 −0.7658 0.27236 SHPLPQCEIVK (26) Knee_JSN_Pheno_P01031_CO5_GIYGTIS −2.6335 0.19845 −2.727 0.18412 R (44) Knee_JSN_Pheno_P51884_LUM_ILGPLSY −2.2931 0.18757 −2.381 0.17292 SK (90) Knee_JSN_Pheno_P01031_CO5_TLLPVSK −2.6148 0.19708 −2.7026 0.18569 PEIR (45) Knee_JSN_Pheno_P07360_CO8G_QLYGD −4.6571 0.35419 −4.8008 0.38448 TGVLGR (52) Knee_JSN_Pheno_P20851_C4BPB_SQCLE −2.4987 0.25857 −2.6078 0.24415 DHTWAPPFPICK (27) Knee_JSN Pheno_P01031_CO5_IIHFGTR −2.4834 0.2052 −2.5725 0.19137 (46) Knee_JSN_Pheno_P05156_CFAI_HGNTDS −2.1353 0.23576 −2.2203 0.2542 EGIVEVK (36) Knee_JSN_Pheno_P02743_SAMP_AYSDL −1.172 0.26186 −1.3063 0.39507 SR (104) Knee_JSN_Pheno_Q9NQ79_CRAC1_SSPY −5.3076 0.32576 −5.4371 0.30832 YALR (2) Knee_JSN_Pheno_Q15113_PCOC1_TGGL −5.0009 0.19706 −5.0763 0.17589 DLPSPPTGASLK (95) Knee_JSN_Pheno_P12259_FA5_SEAYNTF −4.6902 0.23862 −4.7772 0.27625 SER (61) Knee_JSN_Pheno_P51884_LUM_VANEVT −1.3903 0.22821 −1.4664 0.22184 LN (91) Knee_JSN_Pheno_P04278_SHBG_IALGGL −4.7876 0.75918 −4.5138 0.65685 LFPASNLR (105) Knee_KL_Pheno_Q9NQ79_CRAC1_GVAS −5.36 0.30055 −5.634 0.28483 LFAGR (1) Knee_KL_Pheno_Q9NQ79_CRAC1_SSPY −5.2986 0.30834 −5.5093 0.30855 YALR (2) Knee_KL_Pheno_P07360_CO8G_QLYGDT −4.6643 0.36247 −4.8469 0.3723 GVLGR (52) Knee_KL_Pheno_P49747_COMP_NALWH −5.2483 0.40041 −5.5027 0.51099 TGDTESQVR (54) Knee_KL_Pheno_P07358_CO8B_GILNEIK −4.337 0.36071 −4.4394 0.33841 (51) Knee_KL_Pheno_P04004_VTNC_QPQFIS −2.5841 0.32518 −2.4986 0.31075 R(120) Knee_KL_Pheno_P04278_SHBG_IALGGL −4.7795 0.74084 −4.415 0.62606 LFPASNLR (105) Knee_KL_Pheno_P02775_CXCL7_NIQSLE −1.6465 0.43393 −1.8899 0.80079 VIGK (57) Knee_KL_Pheno_Q15113_PC0C1_TGGLD −5.0083 0.20352 −5.0934 0.14771 LPSPPTGASLK (95) Knee_KL_Pheno_P12111_CO6A3_EVQVF −5.7888 0.27059 −5.9159 0.24476 EITENSAK (49) Knee_KL_Pheno_P51884_LUM_ILGPLSY −2.3063 0.18911 −2.3918 0.16554 SK (90) Knee_KL_Pheno_P02743_SAMP_AYSDLS −1.195 0.2826 −1.317 0.41746 R(104) Knee_KL_Pheno_P01031_CO5_IIHFGTR −2.5003 0.21274 −2.5762 0.17348 (46) Knee_KL_Pheno_P51884_LUM_VANEVT −1.4005 0.22997 −1.4781 0.21611 LN (91) Knee_KL_Pheno_P01031_CO5_GIYGTISR −2.6535 0.21307 −2.7265 0.15017 (44) Knee_KL_Pheno_P04003_C4BPA_LSLEIE −0.6435 0.25492 −0.7256 0.18876 QLELQR (25) Knee_OST_Pheno_Q9NQ79_CRAC1_GVA −5.353 0.30661 −5.5993 0.28726 SLFAGR (1) Knee_OST_Pheno_Q9NQ79_CRAC1_SSP −5.2892 0.30785 −5.4886 0.31058 YYALR (2) Knee_OST_Pheno_P04004_VTNC_QPQFIS −2.5963 0.31787 −2.4944 0.32087 R(120) Knee_OST_Pheno_P49747_COMP_NALW −5.2442 0.40004 −5.467 0.50102 HTGDTESQVR (54) Knee_OST_Pheno_P04278_SHBG_IALGG −4.7799 0.74413 −4.4746 0.65493 LLFPASNLR (105) Knee_OST_Pheno_P12111_C06A3_EVQV −5.7842 0.27689 −5.9018 0.24009 FEITENSAK (49) Knee_OST_Pheno_Q15113_PCOC1_TGGL −5.0092 0.20737 −5.078 0.15463 DLPSPPTGASLK (95) MRM markers; non-depleted Serum; person Knee_JSN_person_Pheno_P00450_CERU 0.65572 0.24983 0.76035 0.27569 HYYIAAEEIIWNYAPSGIDIFTK (29) Knee_JSN_person_Pheno_P01031_CO5_GI −2.6381 0.20909 −2.7346 0.16563 YGTISR (44) Knee_JSN_person_Pheno_P01031_CO5_II −2.4887 0.21661 −2.5783 0.17261 HFGTR (46) Knee_JSN_person_Pheno_P01031_C05_TL −2.6187 0.20754 −2.7103 0.16802 LPVSKPEIR (45) Knee_JSN_person_Pheno_P01033_TIMP1 −5.5604 0.40065 −5.7497 0.42285 GFQALGDAADIR (111) Knee_JSN_person_Pheno_P02743_SAMP −1.1745 0.27542 −1.3229 0.39912 AYSDLSR (104) Knee_JSN_person_Pheno_P02775_CXCL7 −1.5899 0.27108 −1.924 0.83168 NIQSLEVIGK (57) Knee_JSN_person_Pheno_P02776_PLF4_IC −2.6915 0.33412 −2.9413 0.78317 LDLQAPLYK (99) Knee_JSN_person_Pheno_P04003_C4BPA −0.6459 0.33432 −0.7677 0.2194 GVGWSHPLPQCEIVK (26) Knee_JSN_person_Pheno_P04003_C4BPA −0.6165 0.25436 −0.7484 0.18988 LSLEIEQLELQR (25) Knee_JSN_person_Pheno_P04217_AlBG_I 0.00845 0.27983 −0.0351 0.17265 FFHLNAVALGDGGHYTCR (3) Knee_JSN_person_Pheno_P04278_SHBG_I −4.7677 0.77275 −4.501 0.62726 ALGGLLFPASNLR (105) Knee_JSN_person_Pheno_P07360_CO8G −4.6695 0.36108 −4.8047 0.38572 QLYGDTGVLGR (52) Knee_JSN_person_Pheno_P07996_TSP1_F −3.9283 0.35544 −4.1322 0.73241 VFGTTPEDILR (112) Knee_JSN_person_Pheno_P12259_FA5_SE −4.6923 0.24286 −4.7873 0.27747 AYNTFSER (61) Knee_JSN_person_Pheno_P20851_C4BPB −2.5112 0.27892 −2.6064 0.21459 SQCLEDHTWAPPFPICK (27) Knee_JSN_person_Pheno_P49747_COMP −5.2389 0.40342 −5.4677 0.49665 NALWHTGDTESQVR (54) Knee_JSN_person_Pheno_P51884_LUM_IL −2.2943 0.18871 −2.3925 0.16777 GPLSYSK (90) Knee_JSN_person_Pheno_P51884_LUM_V −1.3877 0.23105 −1.4815 0.21506 ANEVTLN (91) Knee_JSN_person_Pheno_Q15113_PCOC1 −4.9982 0.19726 −5.0915 0.16919 TGGLDLPSPPTGASLK (95) Knee_JSN_person_Pheno_Q9NQ79_CRAC −5.3647 0.31833 −5.5753 0.28924 1 GVASLFAGR (1) Knee_JSN_person_Pheno_Q9NQ79_CRAC −5.3165 0.32877 −5.4439 0.30529 1 SSPYYALR (2) Knee_KL_person_Pheno_P01031_CO5_GI −2.6531 0.21059 −2.7391 0.14588 YGTISR (44) Knee_KL_person_Pheno_P01031_CO5_IIH −2.4986 0.21063 −2.5922 0.1707 FGTR (46) Knee_KL_person_Pheno_P02743_SAMP_A −1.1947 0.27677 −1.3369 0.44546 YSDLSR (104) Knee_KL_person_Pheno_P02775_CXCL7 −1.6407 0.42587 −1.9424 0.85113 NIQSLEVIGK (57) Knee_KL_person_Pheno_P04278_SHBG_I −4.7405 0.75939 −4.4535 0.59653 ALGGLLFPASNLR (105) Knee_KL_person_Pheno_P05156_CFAI_A −2.3816 0.24298 −2.5067 0.27195 QLGDLPWQVAIK (37) Knee_KL_person_Pheno_P05156_CFAI_H −2.1467 0.24629 −2.247 0.24236 GNTDSEGIVEVK (36) Knee_KL_person_Pheno_P07360_CO8G_Q −4.6628 0.36307 −4.8793 0.36711 LYGDTGVLGR (52) Knee_KL_person_Pheno_P12111_CO6A3 −5.7924 0.26658 −5.927 0.25429 EVQVFEITENSAK (49) Knee_KL_person_Pheno_P12259_FA5_SE −4.7013 0.24808 −4.8058 0.27986 AYNTFSER (61) Knee_KL_person_Pheno_P49747_COMP_N −5.2524 0.39475 −5.5326 0.5373 ALWHTGDTESQVR (54) Knee_KL_person_Pheno_P51884_LUM_IL −2.3066 0.18753 −2.4043 0.16517 GPLSYSK (90) Knee_KL_person_Pheno_P51884_LUM_V −1.3978 0.22966 −1.497 0.21235 ANEVTLN (91) Knee_KL_person_Pheno_Q15113_PCOC1 −5.0045 0.20633 −5.116 0.11555 TGGLDLPSPPTGASLK (95) Knee_KL_person_Pheno_Q9NQ79_CRAC1 −5.365 0.30117 −5.6649 0.27366 GVASLFAGR (1) Knee_KL_person_Pheno_Q9NQ79_CRAC1 −5.2981 0.31394 −5.5437 0.28344 SSPYYALR (2) Knee_KL_person_Pheno_Q9UK55_ZPI_VV −3.0199 0.13919 −3.0653 0.15335 NPTLL (122) Knee_OST_person_Pheno_P02775_CXCL7 −1.6482 0.43797 −1.8835 0.8009 NIQSLEVIGK (57) Knee_OST_person_Pheno_P04004_VTNC −2.5885 0.33293 −2.4911 0.29601 QPQFISR (120) Knee_OST_person_Pheno_P07360_CO8G −4.6736 0.37267 −4.8264 0.36566 QLYGDTGVLGR (52) Knee_OST_person_Pheno_P12111_CO6A3 −5.7868 0.27448 −5.9182 0.2382 EVQVFEITENSAK (49) Knee_OST_person_Pheno_P49747_COMP −5.2438 0.39886 −5.5084 0.51353 NALWHTGDTESQVR (54) Knee_OST_person_Pheno_P63261_ACTG −4.1926 0.28264 −4.0727 0.29266 VAPEEHPVLLTEAPLNPK (11) Knee_OST_person_Pheno_Q15113_PCOC1 −5.0081 0.2103 −5.0927 0.13171 TGGLDLPSPPTGASLK (95) Knee_OST_person_Pheno_Q9NQ79_CRAC −5.357 0.3055 −5.6367 0.27386 1 GVASLFAGR (1) Knee_OST_person_Pheno_Q9NQ79_CRAC −5.2931 0.3177 −5.5175 0.28595 1 SSPYYALR (2) Knee_OST_person_Pheno_Q9UK55_ZPI_V −3.0144 0.12597 −3.0693 0.1705 VNPTLL (122) MRM markers; depleted Serum; person; actin Knee_JSN_person_Pheno_P02776_PLF4_IC 0.01106 0.56919 −0.0167 0.52339 LDLQAPLYK (99) Knee_JSN_person_Pheno_P06396_GELS_G 0.01656 0.24249 −0.0285 0.2353 GVASGFK (72) Knee_JSN_person_Pheno_P08697_A2AP_S 0.20908 0.55165 −0.3041 0.41444 PPGVCSR (4) Knee_JSN_person_Pheno_P08697_A2AP_L −0.1708 0.72306 0.24376 0.29575 CQDLGPGAFR (5) Knee_JSN_person_Pheno_P04114_APOB −0.0027 0.32783 0.01013 0.3457 LAIPEGK (15) Knee_JSN_person_Pheno_P02649_APOE_L 0.08819 0.4891 −0.1192 0.48267 QAEAFQAR (17) Knee_JSN_person_Pheno_P02747_C1QC −0.0366 0.91229 0.08887 0.78024 VVTFCGHTSK (20) Knee_JSN_person_Pheno_P05156_CFAI_A 0.01703 0.2518 −0.0191 0.24696 QLGDLPWQVAIK (37) Knee_JSN_person_Pheno_P06681_CO2_SS −0.1684 0.76581 0.25677 0.27432 GQWQTPGATR (41) Knee_JSN_person_Pheno_P01031_CO5_GI −0.0594 0.43213 0.09809 0.26792 YGTISR (44) Knee_JSN_person_Pheno_P12111_CO6A3 0.05255 0.2943 −0.0797 0.23417 EVQVFEITENSAK (49) Knee_JSN_person_Pheno_P07360_CO8G 0.05261 0.30651 −0.0672 0.31322 QLYGDTGVLGR (52) Knee_JSN_person_Pheno_Q9NQ79_CRAC 0.08623 0.38824 −0.1257 0.34708 1 SSPYYALR (2) Knee_JSN_person_Pheno_Q9NQ79_CRAC 0.09636 0.33552 −0.1408 0.33742 1 GVASLFAGR (1) Knee_JSN_person_Pheno_P12259_FA5_SE −0.0749 0.41656 0.11694 0.26129 AYNTFSER (61) Knee_JSN_person_Pheno_Q12805_FBLN3 0.05341 0.36555 −0.0956 0.29821 ADQVCINLR (65) Knee_JSN_person_Pheno_P01042_KNG1 0.0016 0.76436 0.0537 0.40236 LDDDLEHQGGHVLDHGHK (88) Knee_JSN_person_Pheno_Q15113_PCOC1 0.04065 0.28654 −0.0545 0.28022 TGGLDLPSPPTGASLK (95) Knee_JSN_person_Pheno_P80108_PHLD_F 0.06571 0.6118 −0.1345 0.52321 GSSLITVR (98) Knee_JSN_person_Pheno_P02743_SAMP 0.04589 0.26942 −0.075 0.30542 AYSDLSR (104) Knee_JSN_person_Pheno_P05452_TETN_T 0.02835 0.28053 −0.0469 0.25301 FHEASEDCISR (107) Knee_JSN_person_Pheno_P01033_TIMP1 0.04351 0.34846 −0.052 0.19604 GFQALGDAADIR (111) Knee_JSN_person_Pheno_P49747_COMP −0.1264 0.55906 0.20054 0.42498 SSTGPGEQLR (55) Knee_JSN_person_Pheno_P35443_TSP4_D 0.07752 0.49144 −0.1112 0.35704 VDIDSYPDEELPCSAR (113) Knee_JSN_person_Pheno_Q12805_FBLN3 0.06224 0.39736 −0.1043 0.27103 NPCQDPYILTPENR (64) Knee_KL_person_Pheno_P02776_PLF4_IC 0.01508 0.56529 −0.0374 0.51299 LDLQAPLYK (99) Knee_KL_person_Pheno_P06396_GELS_G 0.01122 0.24343 −0.0334 0.2305 GVASGFK (72) Knee_KL_person_Pheno_P23142_FBLN1 0.01083 0.28993 −0.0576 0.25805 TGYYFDGISR (63) Knee_KL_person_Pheno_P08697_A2AP_S 0.15955 0.57776 −0.3861 0.22829 PPGVCSR (4) Knee_KL_person_Pheno_P08697_A2AP_L −0.1189 0.68515 0.28116 0.2684 CQDLGPGAFR (5) Knee_KL_person_Pheno_P02649_APOE_L 0.07706 0.46658 −0.1739 0.52368 QAEAFQAR (17) Knee_KL_person_Pheno_P02747_C1QC_V −0.0097 0.88292 0.07314 0.81026 VTFCGHTSK (20) Knee_KL_person_Pheno_P16070_CD44_Y 0.01844 0.2285 −0.0494 0.20504 GFIEGHVVIPR (28) Knee_KL_person_Pheno_P08603_CFAH_C 0.02996 0.26309 −0.0792 0.20569 LPVTAPENGK (35) Knee_KL_person_Pheno_P06681_CO2_SS −0.1153 0.72103 0.29553 0.25057 GQWQTPGATR (41) Knee_KL_person_Pheno_P01031_CO5_GI −0.0458 0.42048 0.12717 0.22113 YGTISR (44) Knee_KL_person_Pheno_P12111_CO6A3 0.03803 0.28928 −0.0966 0.22546 EVQVFEITENSAK (49) Knee_KL_person_Pheno_Q9NQ79_CRAC1 0.0889 0.37549 −0.2157 0.32057 SSPYYALR (2) Knee_KL_person_Pheno_Q9NQ79_CRAC1 0.09367 0.3324 −0.2277 0.30339 GVASLFAGR (1) Knee_KL_person_Pheno_P12259_FA5_SE −0.0563 0.40341 0.14727 0.23078 AYNTFSER (61) Knee_KL_person_Pheno_Q12805_FBLN3 0.04385 0.35046 −0.1311 0.30663 ADQVCINLR (65) Knee_KL_person_Pheno_Q15113_PCOC1 0.04197 0.27951 −0.0952 0.28438 TGGLDLPSPPTGASLK (95) Knee_KL_person_Pheno_P01033_TIMP1 0.02734 0.33457 −0.0504 0.17746 GFQALGDAADIR (111) Knee_KL_person_Pheno_P49747_COMP_S −0.08 0.57743 0.21684 0.31976 STGPGEQLR (55) Knee_KL_person_Pheno_P35443_TSP4_D 0.06604 0.47366 −0.1577 0.34252 VDIDSYPDEELPCSAR (113) Knee_KL_person_Pheno_Q12805_FBLN3 0.04932 0.37969 −0.1385 0.26628 NPCQDPYILTPENR (64) Knee_OST_person_Pheno_P06396_GELS 0.01502 0.24089 −0.035 0.23657 GGVASGFK (72) Knee_OST_person_Pheno_P08697_A2AP 0.14605 0.57584 −0.2877 0.39295 SPPGVCSR (4) Knee_OST_person_Pheno_P08697_A2AP −0.1351 0.70192 0.26055 0.27125 LCQDLGPGAFR (5) Knee_OST_person_Pheno_P63261_ACTG −3.0367 0.27282 −2.9771 0.25759 VAPEEHPVLLTEAPLNPK (11) Knee_OST_person_Pheno_P02747_C1QC 0.01463 0.89969 0.01413 0.78594 VVTFCGHTSK (20) Knee_OST_person_Pheno_P06681_CO2_SS −0.1157 0.72985 0.2422 0.34386 GQWQTPGATR (41) Knee_OST_person_Pheno_P01031_CO5_GI −0.0524 0.42324 0.11753 0.24644 YGTISR (44) Knee_OST_person_Pheno_P12111_CO6A3 0.04953 0.26305 −0.1016 0.28306 EVQVFEITENSAK (49) Knee_OST_person_Pheno_Q9NQ79_CRAC 0.08684 0.38283 −0.1715 0.33148 1 SSPYYALR (2) Knee_OST_person_Pheno_Q9NQ79_CRAC 0.09341 0.33665 −0.1849 0.31822 1 GVASLFAGR (1) Knee_OST_person_Pheno_P12259_FA5_SE −0.067 0.40428 0.14172 0.25122 AYNTFSER (61) Knee_OST_person_Pheno_Q12805_FBLN3 0.04391 0.35403 −0.1082 0.31055 ADQVCINLR (65) Knee_OST_person_Pheno_Q15113_PCOC1 0.03371 0.28161 −0.0608 0.2897 TGGLDLPSPPTGASLK (95) Knee_OST_person_Pheno_P05452_TETN 0.01949 0.27559 −0.0453 0.2601 TFHEASEDCISR (107) Knee_OST_person_Pheno_P49747_COMP −0.0702 0.58158 0.15842 0.3793 SSTGPGEQLR (55) Knee_OST_person_Pheno_P35443_TSP4_D 0.0712 0.47712 −0.1384 0.35618 VDIDSYPDEELPCSAR (113) Knee_OST_person_Pheno_Q12805_FBLN3 0.046 0.3822 −0.1073 0.28853 NPCQDPYILTPENR (64) ELISA markers; knee; GEE Knee_JSN_Pheno_cd14_serum 2575.92 697.218 2248.74 613.423 Knee_JSN_Pheno_1HA_serum 5.71988 0.85584 5.11415 0.75264 Knee_JSN_Pheno_cerulo_urine_urine1 0.5915 0.79293 1.01494 0.92315 Knee_JSN_Pheno_kinno_serum 294.282 121.894 399.274 235.284 Knee_JSN_Pheno_cerulo_urine_urine2 −8.792 1.27149 −8.0153 1.54441 Knee_JSN_Pheno_hapto_serum 1333.67 757.365 1072.78 642.54 Knee_JSN_Pheno_ctx1a_ctx_1b_urine2 −15.333 2.20557 −14.262 2.50326 Knee_JSN_Pheno_ctx1a_ctx1b_urine1 −5.9539 1.36704 −5.2837 1.54433 Knee_JSN_Pheno_vitd_binding_serum 939.199 593.361 1196.78 705.038 Knee_KL_Pheno_kinno_serum 295.263 119.804 441.7 259.637 Knee_KL_Pheno_cd14_serum 2546.19 715.028 2169.76 514.811 Knee_KL_Pheno_1HA_serum 5.63111 0.81308 5.01795 0.7993 Knee_KL_Pheno_cerulo_urine_urine2 −8.6707 1.29058 −7.9629 1.63572 Knee_KL_Pheno_ctx1a_ctx_1b_urine2 −15.234 2.22103 −14.065 2.56318 Knee_KL_Pheno_ctx1a_ctx_1b_urine1 −5.8997 1.38261 −5.1219 1.56123 Knee_KL_Pheno_vitd_binding_serum 960.501 602.213 1263.09 725.301 Knee_OST_Pheno_kinno_serum 287.479 113.997 436.257 249.333 Knee_OST_Pheno_cerulo_urine_urine2 −8.6634 1.28494 −8.0895 1.61516 Knee_OST_Pheno_1HA_serum 5.61496 0.81508 5.11291 0.83713 Knee_OST_Pheno_ctx1a_ctx1b_urine2 −15.23 2.21429 −14.263 2.55727 Knee_OST_Pheno_ctx1a_ctx1b_urine1 −5.9052 1.38361 −5.2423 1.55556 Knee_OST_Pheno_cd14_serum 2525.14 699.29 2262.88 613.837 Knee_OST_Pheno_ctx2_urine1 −5.0633 0.67926 −5.3241 0.66827 Knee_OST_Pheno_vitd_binding_serum 969.878 616.484 1204.18 702.73 ELISA markers; knee; person Knee_JSN_person_Pheno_cd14_serum 2602.29 697.192 2162.75 564.587 Knee_JSN_person_Pheno_cerulo_urine_urin 0.57459 0.78943 1.10486 0.92437 e1 Knee_JSN_person_Pheno_cerulo_urine_urin −8.8113 1.25771 −7.8761 1.56237 e2 Knee_JSN_person_Pheno_1HA_serum 5.69895 0.82885 5.06371 0.77171 Knee_JSN_person_Pheno_kinno_serum 294.293 122.788 412.385 243.775 Knee_JSN_person_Pheno_ctx1a_ctx1b_urin  −15.324 2.21534 −14.121 2.52557 e2 Knee_JSN_person_Pheno_ctxi_urine2 −12.991 1.55032 −12.262 1.85502 Knee_JSN_person_Pheno_ctx1a_ctx1b_urin  −5.9456 1.37228 −5.1932 1.55878 e1 Knee_JSN_person_Pheno_ctxi_urine1 −3.613 0.77049 −3.2866 0.82608 Knee_JSN_person_Pheno_vitd_binding_seru 946.698 594.172 1224.92 721.843 m Knee_KL_person_Pheno_kinno_serum 296.711 124.85 455.818 265.551 Knee_KL_person_Pheno_cd14_serum 2530.04 710.539 2149.83 508.765 Knee_KL_person_Pheno_1HA_serum 5.61727 0.80017 4.97511 0.83057 Knee_KL_person_Pheno_cerulo_urine_urine −8.6167 1.33156 −7.9782 1.65722 2 Knee_KL_person_Pheno_vitd_binding_seru 981.978 620.479 1258.48 725.875 m Knee_KL_person_Pheno_ctx1a_ctx1b_urine −5.8193 1.4108 −5.1917 1.60826 1 Knee_OST_person_Pheno_kinno_serum 291.557 123.07 452.669 256.211 Knee_OST_person_Pheno_cd14_serum 2520.46 709.306 2223.94 573.96 Knee_OST_person_Pheno_1HA_serum 5.601 0.79819 5.05754 0.87099 Knee_OST_person_Pheno_ctx1a_ctx1b_urin −5.8403 1.42891 −5.2499 1.55116 e1

TABLE 2D Prognostic Data (P = Progressor, NP = non−Progressor) Prognostic Data Phenotype (SEQ ID NO: X) Pmean Psd NPmean NPsd MRM markers; nondepleted serum; knee; GEE Knee_JSN_Pheno_P08603_CFAH_CLPVT −1.8733 0.18394 −1.9774 0.25529 APENGK (35) Knee_JSN Pheno_P02749_APOH_ATFGC 0.21648 0.18367 0.10653 0.27925 HDGYSLDGPEEIECTK (18) Knee_JSN Pheno_P04196_HRG_YWNDC −2.3987 0.25856 −2.4794 0.35586 EPPDSR (81) Knee_JSN_Pheno_P01011_AACT_NLAVS 0.06855 0.2493 −0.0534 0.25394 QVVHK (9) Knee_JSN_Pheno_P01011_AACT_ADLSG 0.00169 0.23843 −0.1136 0.23088 ITGAR (8) Knee_JSN_Pheno_P02753_RET4_LIVHN −1.328 0.26527 −1.4141 0.29284 GYCDGR (103) Knee_JSN_Pheno_P02743_SAMP_AYSDL −1.1083 0.2406 −1.2255 0.26865 SR (104) Knee_JSN_Pheno_P00734_THRB_NPDSS −1.2674 0.3453 −1.399 0.36795 TTGPWCYTTDPTVR (110) Knee_JSN_Pheno_P01011_AACT_EQLSL 0.64266 0.24252 0.52223 0.25274 LDR (10) Knee_JSN_Pheno_Q14624_ITIH4_FKPTL −1.1111 0.21404 −1.1857 0.24724 SQQQK (86) Knee_JSN_Pheno_P07996_TSP1_FVFGTT −3.8418 0.26133 −3.9693 0.37682 PEDILR (112) Knee_JSN_Pheno_P16112_PGCA_VSLPN −8.3507 0.5464 −8.6397 0.6233 YPAIPSDATLEVQSLR (96) Knee_KL_Pheno_P08603_CFAH_CLPVT −1.8514 0.15151 −1.9889 0.26845 APENGK (35) Knee_KL_Pheno_P80108_PHLD_FGSSLI −3.8693 0.223 −3.9931 0.33846 TVR (98) Knee_KL_Pheno_P02743_SAMP_AYSDL −1.0849 0.2601 −1.2417 0.27982 SR (104) Knee_KL_Pheno_P01031_CO5_TLLPVSK −2.5743 0.17657 −2.6595 0.21099 PER (45) Knee_KL_Pheno_P01031_CO5_GIYGTIS −2.586 0.17701 −2.6822 0.22119 R (44) Knee_KL_Pheno_P04003_C4BPA_GVGW −0.6009 0.26167 −0.7042 0.35265 SHPLPQCEIVK (26) Knee_KL_Pheno_P02753_RET4_LIVHNG −1.318 0.28743 −1.405 0.27131 YCDGR (103) Knee_KL_Pheno_P02749_APOH_ATFGC 0.21968 0.20833 0.12004 0.27085 HDGYSLDGPEEIECTK (18) Knee_KL_Pheno_P07996_TSP1_FVFGTT −3.818 0.24999 −4.0312 0.51382 PEDILR (112) Knee_KL_Pheno_P02765_FETUA_FSVV 0.47776 0.1871 0.42755 0.18126 YAK (69) Knee_KL_Pheno_P00734_THRB_NPDSST −1.2604 0.36827 −1.4032 0.36302 TGPWCYTTDPTVR (110) Knee_KL_Pheno_P00736_C1R_GLTLHLK −1.5768 0.17668 −1.6834 0.23557 (22) Knee_KL_Pheno_P01011_AACT_NLAVS 0.09174 0.25744 −0.0404 0.25316 QVVHK (9) Knee_KL_Pheno_P00736_C1R_GYGFYT −2.4711 0.1838 −2.5691 0.21992 K(23) Knee_KL_Pheno_P09871_C1S_LLEVPEG −2.3873 0.16142 −2.4663 0.17408 R(39) Knee_KL_Pheno_P04114_APOB_LAIPEG −1.1379 0.23634 −1.2751 0.2705 K(15) Knee_KL_Pheno_P01011_AACT_ADLSGI 0.0238 0.24725 −0.0937 0.22724 TGAR (8) Knee_KL_Pheno_P05546_HEP2_NFGYTL −0.3486 0.23541 −0.4828 0.22968 R(76) Knee_KL_Pheno_P05156_CFAI_HGNTDS −2.0777 0.19407 −2.1776 0.26138 EGIVEVK (36) Knee_KL_Pheno_P02751_FINC_EYLGAI −1.5794 0.37426 −1.7745 0.50563 CSCTCFGGQR (70) Knee_KL_Pheno_P02751_FINC_IGDTWS −1.9724 0.28416 −2.1392 0.44287 K(71) Knee_OST_Pheno_P02776_PLF4_ICLDLQ −2.6274 0.32402 −2.8927 0.54107 APLYK (99) Knee_OST_Pheno_P01011_AACT_EQLSL 0.63078 0.24078 0.5271 0.24313 LDR (10) Knee_OST_Pheno_P01011_AACT_ADLS −0.0125 0.22315 −0.1117 0.22687 GITGAR (8) Knee_OST_Pheno_P01008_ANT3_ATEDE 5.43145 0.39014 5.62562 0.4794 GSEQK (14) Knee_OST_Pheno_P01011_AACT_NLAV 0.04359 0.24761 −0.0522 0.24691 SQVVHK (9) Knee_OST_Pheno_P02775_CXCL7_NIQS −1.5495 0.28583 −1.7827 0.54969 LEVIGK (57) Knee_OST_Pheno_Q9NQ79_CRAC1_SSP −5.2482 0.31894 −5.3319 0.29197 YYALR (2) Knee_OST_Pheno_Q14624_ITIH4_FKPTL −1.1228 0.21444 −1.2061 0.25212 SQQQK (86) Knee_OST_Pheno_P00747_PLMN_HSIFT −0.9833 0.22029 −1.0623 0.23109 PETNPR (100) Knee_OST_Pheno_P07996_TSP1_FVFGT −3.8992 0.3198 −4.0688 0.58725 TPEDILR (112) Knee_OST_Pheno_P01031_CO5_GIYGTIS −2.6246 0.18727 −2.6943 0.23962 R (44) Knee_OST_Pheno_P00734_THRB_NPDSS −1.3017 0.35587 −1.4473 0.37127 TTGPWCYTTDPTVR (110) Knee_OST_Pheno_P04003_C4BPA_GVG −0.6339 0.29939 −0.7455 0.37048 WSHPLPQCEIVK (26) MRM markers; nondepleted serum; knee; person Knee_JSN_person_Pheno_P04196_HRG_Y −2.3884 0.28937 −2.5389 0.3736 WNDCEPPDSR (81) Knee_JSN_person_Pheno_P08603_CFAH −1.8864 0.19324 −2.0163 0.29792 CLPVTAPENGK (35) Knee_JSN_person_Pheno_P00734_THRB −1.2579 0.34881 −1.4555 0.39541 NPDSSTTGPWCYTTDPTVR (110) Knee_JSN_person_Pheno_P02749_APOH 0.21115 0.20133 0.09183 0.30327 ATFGCHDGYSLDGPEEIECTK (18) Knee_JSN_person_Pheno_P07996_TSP1_F −3.8365 0.27116 −4.0282 0.40996 VFGTTPEDILR (112) Knee_JSN_person_Pheno_P01011_AACT −0.0089 0.24667 −0.1122 0.22344 ADLSGITGAR (8) Knee_KL_person_Pheno_P08603_CFAH −1.8612 0.15652 −2.0084 0.27151 CLPVTAPENGK (35) Knee_KL_person_Pheno_P04003_C4BPA −0.5958 0.26047 −0.7189 0.35023 GVGWSHPLPQCEIVK (26) Knee_KL_person_Pheno_P00734_THRB −1.2649 0.35943 −1.4086 0.36133 NPDSSTTGPWCYTTDPTVR (110) Knee_KL_person_Pheno_P02743_SAMP −1.0876 0.27091 −1.2492 0.26573 AYSDLSR (104) Knee_KL_person_Pheno_P07996_TSP1_F −3.8132 0.25135 −4.0374 0.51953 VFGTTPEDILR (112) Knee_KL_person_Pheno_P01033_TIMP1 −5.4698 0.473 −5.666 0.36542 GFQALGDAADIR (111) Knee_KL_person_Pheno_P01011_AACT 0.0924 0.26356 −0.0471 0.24199 NLAVSQVVHK (9) Knee_KL_person_Pheno_P01011_AACT 0.02557 0.24965 −0.1008 0.21807 ADLSGITGAR (8) Knee_KL_person_Pheno_P00736_C1R_GL −1.5842 0.19482 −1.6865 0.22542 TLHLK (22) Knee_KL_person_Pheno_P00736_C1R_GY −2.474 0.19643 −2.572 0.20978 GFYTK (23) Knee_KL_person_Pheno_P04114_APOB_L −1.1494 0.23247 −1.2804 0.26849 AIPEGK (15) Knee_KL_person_Pheno_P01011_AACT 0.662 0.25482 0.54442 0.24088 EQLSLLDR (10) Knee_KL_person_Pheno_P05546_HEP2_F −1.0844 0.30497 −1.2083 0.25962 TVDRPFLFLIYEHR (77) Knee_KL_person_Pheno_P05546_HEP2_N −0.3694 0.24506 −0.4742 0.2235 FGYTLR (76) Knee_OST_person_Pheno_P02776_PLF4_I −2.6369 0.35736 −2.9409 0.55151 CLDLQAPLYK (99) Knee_OST_person_Pheno_P02775_CXCL7 −1.556 0.31036 −1.8293 0.58207 NIQSLEVIGK (57) Knee_OST_person_Pheno_P07358_CO8B −4.2578 0.31545 −4.5185 0.43523 GILNEIK (51) Knee_OST_person_Pheno_P01008_ANT3 5.44905 0.40304 5.69097 0.46458 ATEDEGSEQK (14) Knee_OST_person_Pheno_P02751_FINC −1.6448 0.41689 −1.8881 0.56808 EYLGAICSCTCFGGQR (70) Knee_OST_person_Pheno_P02751_FINC_I −2.0429 0.42336 −2.2089 0.36074 GDTWSK (71) Knee_OST_person_Pheno_P07996_TSP1 −3.8963 0.33583 −4.1239 0.64185 FVFGTTPEDILR (112) Knee_OST_person_Pheno_P07360_CO8G −4.6125 0.3262 −4.7935 0.43203 QLYGDTGVLGR (52) Knee_OST_person_Pheno_Q92954_PRG4 −4.4216 0.33795 −4.6091 0.37538 DQYYNIDVPSR (102) Knee_OST_person_Pheno_Q92954_PRG4 −4.087 0.38455 −4.3242 0.46606 ITEVWGIPSPIDTVFTR (101) Knee_OST_person_Pheno_P03952_KLKB1 −1.9461 0.24557 −2.097 0.31923 VSEGNHDIALIK (87) Knee_OST_person_Pheno_P04196_HRG −2.4096 0.26503 −2.5521 0.35484 YWNDCEPPDSR (81) Knee_OST_person_Pheno_P08603_CFAH −1.9136 0.21584 −2.0406 0.29389 CLPVTAPENGK (35) Knee_OST_person_Pheno_P04003_C4BPA −0.6346 0.32245 −0.804 0.33093 GVGWSHPLPQCEIVK (26) Knee_OST_person_Pheno_P06681_CO2_D −3.1113 0.23486 −3.2284 0.23677 GNDHSLWR (42) ELISA markers; knee; GEE Knee_JSN_Pheno_hapto_serum 1606.88 817.954 1107.57 624.568 Knee_JSN_Pheno_cd44_serum 150.65 27.9062 167.947 45.6712 Knee_JSN_Pheno_hemopexin_serum 1550.7 222.741 1419.74 270.341 Knee_JSN_Pheno_cd163_serum 821.961 284.381 881.345 302.904 Knee_JSN_Pheno_vitd_binding_serum 809.959 554.359 1047.53 607.144 Knee_JSN_Pheno_lcerulo_serum 6.58372 0.6484 6.80215 0.64846 Knee_JSN_Pheno_1HA_serum 5.63002 0.93682 5.79114 0.78678 Knee_JSN_Pheno_tbg_serum 15.2226 5.21314 16.5391 4.14235 Knee_JSN_Pheno_ctx_lbeta_urine1 −3.0961 0.84019 −3.2716 0.86223 Knee_JSN_Pheno_ctx1a_ctx1b_urine1 −6.0929 1.3862 −5.8491 1.3537 Knee_JSN_Pheno_cd14_serum 2581.92 660.618 2571.06 730.345 Knee_JSN_Pheno_comp_serum 1624.31 586.104 1666.57 614.388 Knee_JSN_Pheno_hapto_urine_urine1 −1.8209 1.93942 −2.104 1.75651 Knee_JSN_Pheno_hapto_urine_urine2 −11.306 2.02314 −11.512 1.87493 Knee_JSN_Pheno_ctx1a_ctx1b_urine2 −15.535 2.27897 −15.179 2.15288 Knee_JSN_Pheno_ctx_lbeta_urine2 −12.538 1.23799 −12.603 1.30309 Knee_JSN_Pheno_ctx2_urine1 −5.007 0.89173 −5.1513 0.59994 Knee_JSN_Pheno_ctx2_urine2 −14.449 1.04709 −14.485 1.13908 Knee_JSN_Pheno_ctxi_urinel −3.6442 0.78839 −3.5905 0.75005 Knee_JSN_Pheno_ctxi_urine2 −13.087 1.59755 −12.921 1.54536 Knee_JSN_Pheno_coll3_serum 23.7345 3.40834 23.5759 3.41947 Knee_JSN_Pheno_cerulo_urine_urine2 −8.767 1.30124 −8.8112 1.25825 Knee_JSN_Pheno_kinno_serum 290.915 142.942 297.069 102.493 Knee_JSN_Pheno_cerulo_urine_urine1 0.67532 0.8622 0.52831 0.73697 Knee_KL_Pheno_lcerulo_serum_serum 6.51632 0.69437 6.87555 0.60683 Knee_KL_Pheno_hapto_serum 1579.83 865.189 1144.89 625.329 Knee_KL_Pheno_cd44_serum 155.42 35.625 167.649 42.8249 Knee_KL_Pheno_hapto_urine_urine2 −10.901 2.05767 −11.662 1.90504 Knee_KL_Pheno_vitd_binding_serum 905.41 563.381 983.619 618.757 Knee_KL_Pheno_ctx_lbeta_urine2 −12.416 1.4272 −12.628 1.20552 Knee_KL_Pheno_ctx2_urine1 −5.2374 0.91413 −5.0238 0.64339 Knee_KL_Pheno_tbg_serum 16.1841 4.51185 16.4486 4.63061 Knee_KL_Pheno_cd14_serum 2661.92 691.486 2499.09 722.077 Knee_KL_Pheno_ctx1a_ctxlb_urine2 −14.695 2.6064 −15.428 2.04488 Knee_KL_Pheno_ctxi_urine2 −12.666 1.85633 −13.046 1.42449 Knee_KL_Pheno_cerulo_urine_urine1 0.73544 0.86068 0.67915 0.82822 Knee_KL_Pheno_hapto_urine_urine1 −1.7817 1.86541 −2.1445 1.86244 Knee_KL_Pheno_ctx2_urine2 −14.334 1.12639 −14.478 1.10132 Knee_KL_Pheno_cerulo_urine_urine2 −8.3612 1.43515 −8.7819 1.22296 Knee_KL_Pheno_cd163_serum 890.667 308.31 827.306 270.548 Knee_KL_Pheno_comp_serum 1805.5 696.916 1603.06 549.065 Knee_KL_Pheno_kinno_serum 306.848 150.277 290.86 106.542 Knee_KL_Pheno_1HA_serum 5.59659 0.86984 5.64353 0.79587 Knee_KL_Pheno_hemopexin_serum 1589.28 253.919 1477.24 372.807 Knee_KL_Pheno_ctx1a_ctx1b_urine1 −5.6508 1.58585 −5.9889 1.29875 Knee_KL_Pheno_coll3_serum 24.4897 3.53911 23.6109 3.59543 Knee_KL_Pheno_ctxi_urine1 −3.5684 0.91432 −3.5921 0.69628 Knee_KL Pheno ctx lbeta urine1 −3.2978 0.91008 −3.169 0.85943 Knee_OST_Pheno_1HA_serum 5.47246 0.88898 5.76204 0.70844 Knee_OST_Pheno_hapto_serum 1386.54 779.479 1103.49 611.724 Knee_OST_Pheno_cd163_serum 794.197 276.997 892.772 285.564 Knee_OST_Pheno_cd44_serum 158.26 37.0897 172.328 44.3459 Knee_OST_Pheno_tbg_serum 15.7036 5.16053 16.9976 4.04741 Knee_OST_Pheno_cerulo_urine_urine2 −8.8172 1.30041 −8.5072 1.26027 Knee_OST_Pheno_ctx1a_ctx1b_urine1 −6.0859 1.30181 −5.7273 1.44766 Knee_OST_Pheno_ctxi_urine1 −3.6674 0.79932 −3.5154 0.72453 Knee_OST_Pheno_comp_serum 1565.03 476.409 1736.8 679.457 Knee_OST_Pheno_hemopexin_serum 1546.7 357.536 1481.31 359.671 Knee_OST_Pheno_ctx1a_ctx1b_urine2 −15.493 2.15187 −14.962 2.26187 Knee_OST_Pheno_ctxi_urine2 −13.101 1.60025 −12.784 1.49467 Knee_OST_Pheno_cerulo_urine_urine1 0.61404 0.8256 0.77457 0.8674 Knee_OST_Pheno_vitd_binding_serum 1004.24 643.216 934.067 589.759 Knee_OST_Pheno_cd14_serum 2535.59 667.835 2514.55 734.427 Knee_OST_Pheno_ctx_lbeta_urine2 −12.619 1.34067 −12.536 1.23706 Knee_OST_Pheno_ctx2_urine2 −14.451 1.0706 −14.383 1.10635 Knee_OST_Pheno_kinno_serum 291.233 113.953 283.484 114.836 Knee_OST_Pheno_hapto_urine_urine2 −11.556 2.16059 −11.38 1.86644 Knee_OST_Pheno_ctx2_urine1 −5.0195 0.72808 −5.1065 0.63031 Knee_OST_Pheno_lcerulo_serum 6.75596 0.61035 6.83172 0.67827 Knee_OST_Pheno_hapto_urine_urinel −2.0969 1.86438 −2.0368 1.86182 Knee_OST_Pheno_coll3_serum 23.707 3.64127 24.2162 3.52646 Knee_OST_Pheno_ctx_lbeta_urine1 −3.177 0.88927 −3.2504 0.90828 ELISA markers; knee; person Knee_JSN_person_Pheno_hapto_serum 1603.35 829.077 996.543 480.362 Knee_JSN_person_Pheno_cd44_serum 152.974 28.6622 171.635 47.738 Knee_JSN_person_Pheno_lcerulo_serum_s 6.55994 0.64513 6.88947 0.63355 erum Knee_JSN_person_Pheno_vitd_binding_ser  824.727 563.488 1075.84 606.629 UM Knee_JSN_person_Pheno_hemopexin_seru 1529.95 229.779 1415.63 301.975 m Knee_JSN_person_Pheno_cd163_serum 814.817 288.557 887.965 320.78 Knee_JSN_person_Pheno_tbg_serum 15.2671 4.85939 16.9759 4.25425 Knee_JSN_person_Pheno_hapto_urine_urin  −1.6798 1.87231 −2.2666 1.73415 e1 Knee_JSN_person_Pheno_hapto_urine_urin  −11.113 1.984 −11.68 1.81184 e2 Knee_JSN_person_Pheno_1HA_serum 5.61905 0.89867 5.78457 0.75377 Knee_JSN_person_Pheno_cd14_serum 2642.04 662.952 2560.2 739.359 Knee_JSN_person_Pheno_ctx2_urine1 −4.9732 0.82227 −5.2162 0.63565 Knee_JSN_person_Pheno_ctx_lbeta_urine1 −3.0907 0.88169 −3.313 0.90923 Knee_JSN_person_Pheno_ctx2_urine2 −14.351 1.11176 −14.602 1.10084 Knee_JSN_person_Pheno_ctx_lbeta_urine2 −12.469 1.28896 −12.694 1.26058 Knee_JSN_person_Pheno_cerulo_urine_uri 0.6837 0.90088 0.46547 0.656 ne1 Knee_JSN_person_Pheno_cerulo_urine_uri −8.6944 1.39072 −8.9319 1.11398 ne2 Knee_JSN_person_Pheno_ctx1a_ctx1b_urin −6.0306 1.43529 −5.8607 1.32367 e1 Knee_JSN_person_Pheno_ctxi_urine2 −13.005 1.6566 −12.977 1.4598 Knee_JSN_person_Pheno_comp_serum 1679.54 599.743 1633.04 649.199 Knee_JSN_person_Pheno_ctx1a_ctx1b_urin −15.409 2.3739 −15.236 2.07455 e2 Knee_JSN_person_Pheno_ctxi_urine1 −3.6268 0.81623 −3.5992 0.73473 Knee_JSN_person_Pheno_coll3_serum 23.6763 3.52326 23.7218 3.58698 Knee_JSN_person_Pheno_kinno_serum 291.262 138.289 297.648 105.434 Knee_KL_person_Pheno_hapto_serum 1600.51 862.418 1130.33 601.341 Knee_KL_person_Pheno_lcerulo_serum_se 6.58129 0.69665 6.87527 0.62344 rum Knee_KL_person_Pheno_vitd_binding_seru 863.701 542.634 1040.08 651.991 m Knee_KL_person_Pheno_cd44_serum 157.132 40.4298 167.18 40.7173 Knee_KL_person_Pheno_hapto_urine_urine −10.809 2.00864 −11.637 1.93665 2 Knee_KL_person_Pheno_ctx_lbeta_urine2 −12.378 1.37454 −12.639 1.23375 Knee_KL_person_Pheno_tbg_serum 16.0036 4.84965 16.8026 4.64742 Knee_KL_person_Pheno_cd14_serum 2636.39 682.689 2477.81 723.986 Knee_KL_person_Pheno_hapto_urine_urine −1.6427 1.80641 −2.1936 1.86267 1 Knee_KL_person_Pheno_cd163_serum 863.405 311.188 828.269 274.802 Knee_KL_person_Pheno_ctxi_urine2 −12.642 1.7623 −12.988 1.4886 Knee_KL_person_Pheno_ctx1a_ctx1b_urin −14.701 2.49009 −15.301 2.14653 e2 Knee_KL_person_Pheno_ctx2_urine2 −14.272 1.10587 −14.488 1.16679 Knee_KL_person_Pheno_cerulo_urine_urin −8.3301 1.46313 −8.7435 1.26323 e2 Knee_KL_person_Pheno_comp_serum 1794.71 730.993 1613.72 541.515 Knee_KL_person_Pheno_ctx2_urine1 −5.1635 0.88338 −5.0676 0.67814 Knee_KL_person_Pheno_cerulo_urine_urin 0.77872 0.94037 0.68227 0.79315 e1 Knee_KL_person_Pheno_hemopexin_seru 1563.53 274.345 1474.19 372.59 m Knee_KL_person_Pheno_ctxlbeta_urine1 −3.2369 0.88412 −3.2204 0.87136 Knee_KL_person_Pheno_coll3_serum 24.4696 3.42393 23.524 3.72776 Knee_KL_person_Pheno_kinno_serum 300.616 147.734 295.027 115.194 Knee_KL_person_Pheno_ctx1a_ctx1b_urin −5.6758 1.5659 −5.8843 1.34559 e1 Knee_KL_person_Pheno_1HA_serum 5.66124 0.89792 5.59917 0.76519 Knee_KL_person_Pheno_ctxi_urine1 −3.5329 0.8692 −3.5709 0.72404 Knee_OST_person_Pheno_hapto_serum 1322.97 762.45 1107.02 566.902 Knee_OST_person_Pheno_ctxi_urine1 −3.658 0.80638 −3.393 0.69443 Knee_OST_person_Pheno_hemopexin_seru 1550.32 394.039 1417.66 240.545 m Knee_OST_person_Pheno_ctx1a_ctx1b _uri −6.0124 1.35475 −5.5033 1.53767 ne1 Knee_OST_person_Pheno_tbg_serum 16.1968 4.99907 17.3638 4.31623 Knee_OST_person_Pheno_ctxi_urine2 −13.038 1.6066 −12.509 1.54218 Knee_OST_person_Pheno_cerulo_urine_uri −8.754 1.31091 −8.2981 1.39088 ne2 Knee_OST_person_Pheno_ctx1a_ctx1b _un −15.355 2.20092 −14.61 2.43526 ne2 Knee_OST_person_Pheno_comp_serum 1590.05 483.543 1772.73 757.186 Knee_OST_person_Pheno_ctx_lbeta_urine2 −12.637 1.33736 −12.307 1.18124 Knee_OST_person_Pheno_cd14_serum 2592.97 695.535 2380.79 727.765 Knee_OST_person_Pheno_cerulo_urine_uri 0.62306 0.85367 0.83754 0.8564 ne1 Knee_OST_person_Pheno_ctx2_urine2 −14.458 1.06195 −14.214 1.25659 Knee_OST_person_Pheno_1HA_serum 5.56184 0.86152 5.67931 0.66442 Knee_OST_person_Pheno_kinno_serum 301.275 116.42 271.699 136.201 Knee_OST_person_Pheno_vitd_binding_ser 1018.49 635.383 918.883 609.159 um Knee_OST_person_Pheno_coll3_serum 23.8769 3.69506 23.7534 3.49101 Knee_OST_person_Pheno_cd44_serum 161.529 39.9551 167.546 43.4789 Knee_OST_person_Pheno_cd163_serum 834.289 292.216 837.768 287.367 Knee_OST_person_Pheno_ctx_lbeta_urine1 −3.2442 0.9187 −3.183 0.81553 Knee_OST_person_Pheno_hapto_urine _un −11.442 2.13675 −11.4 1.75876 ne2 Knee_OST_person_Pheno_hapto_urine _un −2.0095 1.86826 −2.2442 1.8478 ne1 Knee_OST_person_Pheno_lcerulo_serum_s 6.78475 0.6028 6.76521 0.72645 erum Knee_OST_person_Pheno_ctx2_urine1 −5.0812 0.75831 −5.0836 0.7278 2a. Non-Depleted Serum Proteomics (Analysis Using Calculated Ratios)

2a.1. Progression Analysis

The most significant results are summarized in Tables 3-4 below; these are the results on which the multimarker AUC calculations are based. These Tables list the AUCs achieved in ROC curves for the biomarker alone—AUC_(BM), and the AUC for the full model achieved for the biomarker with demographics (age, gender, BMI and cohort)—AUC_(full), and their corresponding p values.

In brief, markers were identified that could identify JSN progression modestly (best single biomarker AUC 0.65; multimarker AUC 0.55) and OST more strongly (best single biomarker AUC 0.67; multimarker AUC 0.61). Considering only the biomarker (peptide) capability and prediction of knee level progression, 6 peptides achieved AUC ≥0.65 for JSN progression (PGCA, APOH, AACT ×3 peptides, and PHLD), 2 peptides for OST progression (PLF4 and CSCL7), and 6 peptides for KL progression (CFAH, SAMP, HEP2 ×2 peptides, C1R, APOB). The multimarker AUCs for person level progression were somewhat stronger than for knee level progression (Table 3 compared with Table 4) with multimarker AUC 0.67 for OST progression. Considering only the biomarker (peptide) capability and prediction of person level progression, the highest single biomarker AUC was achieved for CXCL7 with AUC 0.70 for prediction of OST. AUC_(BM) in the tables below refers to the AUC with the biomarker alone; AUC_(full) in the tables refers to the AUC calculated when the biomarker and the age, gender and BMI of the subject were considered.

TABLE 3 Top 10 peptides for prediction of knee level Progression from non-depleted serum. Multimarker AUCs based on top 8 peptides. Peptides Peptides Peptides predicting predicting predicting OST (SEQ JSN (SEQ KL (SEQ ID NO: X) ID NO: X) ID NO: X) OST AUCs and Multi- JSN AUCs and Multi- KL AUCs and Multi- p values for marker p values for marker p values for marker peptides AUC = 0.61 peptides AUC = 0.55 peptides AUC = 0.50 0.67_(BM)/0.69_(full) PLF4 (99) 0.62_(BM)/0.70_(full) PGCA (96) 0.67_(BM)/0.82_(full) CFAH (35) (p = 0.001/0.002) (p = 0.019/0.076) (p = 0.003/0.004) 0.67_(BM)/0.67_(full) CXCL7 (57) 0.62_(BM)/0.73_(full) APOH (18) 0.65_(BM)/0.79_(full) SAMP (104) (p = 0.0099/0.013) (p = 0.029/0.016) (p = 0.009/0.018) 0.62_(BM)/0.63_(full) ANT3 (14) 0.61_(BM)/0.70_(full) SAMP (104) 0.62_(BM)/0.80_(full) TSP1 (112) (p = 0.012/0.006) (p = 0.011/0.027) (p = 0.016/0.029) 0.62_(BM)/0.65_(full) AACT (8) 0.65_(BM)/0.71_(full) AACT (10) 0.66_(BM)/0.79_(full) HEP2 (76) (p = 0.008/0.003) (p = 0.029/0.044) (p = 0.021/0.138) 0.63_(BM)/0.65_(full) AACT (10) 0.64_(BM)/0.71_(full) AACT (8) 0.65_(BM)/0.80_(full) C1R (22) (p = 0.01/0.002) (p = 0.021/0.023) (p = 0.022/0.035) 0.63_(BM)/0.62_(full) THRB (110) 0.63_(BM)/0.65_(full) AACT (9) 0.65_(BM)/0.77_(full) APOB (15) (p = 0.031/0.053) (p = 0.01/0.002) (p = 0.019/0.073) 0.61_(BM)/0.63_(full) AACT (9) 0.63_(BM)/0.71_(full) CFAH (35) 0.63_(BM)/0.76_(full) FINC (71) (p = 0.017/0.008) (p = 0.015/0.021) (p = 0.039/0.74) 0.59_(BM)/0.62_(full) ITIH4 (86) 0.66_(BM)/0.69_(full) PHLD (98) 0.66_(BM)/0.80_(full) HEP2 (77) (p = 0.0397/0.040) (p = 0.065/0.075) (p = 0.061/0.173) 0.59_(BM)/0.62_(full) CO8B (51) 0.59_(BM)/0.68_(full) TSP1 (112) 0.61_(BM)/0.77_(full) PGCA (96) (p = 0.061/0.063) (p = 0.040/0.139) (p = 0.072/0.439) 0.60_(BM)/0.62_(full) PLMN (100) 0.64_(BM)/0.71_(full) THRB (110) 0.62_(BM)/0.77_(full) FINC (70) (p = 0.046/0.044) (p = 0.068/0.041) (p = 0.044/0.33) These multimarker AUCs are based on the top 8 peptides and cross-validated as described in the statistical methods. BM is the AUC for the biomarker alone; full is the AUC for the biomarker plus demographics (age, gender, and BMI).

TABLE 4 Top 8 peptides for prediction of person level Progression from non-depleted serum. Peptides Peptides Peptides predicting predicting predicting OST (SEQ JSN (SEQ KL (SEQ ID NO: X) ID NO: X) ID NO: X) OST AUCs and Multi- JSN AUCs and Multi- KL AUCs and Multi- p values for marker p values for marker p values for marker peptides AUC = 0.67 peptides AUC = 0.50 peptides AUC = 0.57 0.69_(BM)/0.70_(full) CO8B (51) 0.63_(BM)/0.70_(full) TSP1 (112) 0.68_(BM)/0.69_(full) CFAH (35) (p = 0.003/0.007) (p = 0.020/0.038) (p = 0.005/0.005) 0.70_(BM)/0.71_(full) PLF4 (99) 0.66_(BM)/0.70_(full) CFAH (35) 0.62_(BM)/0.64_(full) TSP1 (112) (p = 0.004/0.005) (p = 0.025/0.015) (p = 0.014/0.044) 0.66_(BM)/0.67_(full) PRG4 (101) 0.69_(BM)/0.69_(full) THRB (110) 0.67_(BM)/0.67_(full) SAMP (104) (p = 0.017/0.059) (p = 0.025/0.027) (p = 0.007/0.038) 0.64_(BM)/0.66_(full) PRG4 (102) 0.59_(BM)/0.68_(full) HRG (81) 0.65_(BM)/0.68_(full) APOB (15) (p = 0.024/0.055) (p = 0.054/0.011) (p = 0.027/0.128) 0.64_(BM)/0.71_(full) ANT3 (14) 0.62_(BM)/0.68_(full) APOH (18) 0.64_(BM)/0.67_(full) AACT (9) (p = 0.018/0.019) (p = 0.048/0.029) (p = 0.015/0.049) 0.64_(BM)/0.66_(full) C4BPA (26) 0.62_(BM)/0.67_(full) AACT (8) 0.64_(BM)/0.66_(full) AACT (8) (p = 0.029/0.122) (p = 0.064/0.042) (p = 0.017/0.061) 0.71_(BM)/0.73_(full) CXCL7 (57) 0.66_(BM)/0.67_(full) PHLD (98) 0.66_(BM)/0.68_(full) C1R (22) (p = 0.006/0.007) (p = 0.054/0.080) (p = 0.032/0.083) 0.63_(BM)/0.66_(full) C4BPA (25) 0.60_(BM)/0.67_(full) AACT (9) 0.66_(BM)/0.67_(full) C1R (23) (p = 0.051/0.187) (p = 0.079/0.059) (p = 0.031/0.095) These multimarker AUCs are based on the top 8 peptides and cross-validated as described in the statistical methods. BM is the AUC for the biomarker alone; full is the AUC for the biomarker plus demographics (age, gender, and BMI).

2a.2. Diagnostic Analysis—

By multimarker cross-validated AUCs, markers were identified that could diagnose quite strongly at a knee level and person level, all definitions of Osteoarthritis including JSN (AUC 0.71 knee level, 0.66 person level), OST (AUC 0.70 knee level and person level) and KL grade (0.77 knee level and 0.74 person level). Considering only the biomarker (peptide) capability and prediction of knee level diagnosis, 2 peptides achieved AUC ≥0.65 for a JSN diagnosis (CRTAC1 (also denoted as CRAC1) and CO5), 2 peptides for an OST diagnosis (CRTAC1 ×2 peptides), and 3 peptides for a KL based diagnosis (CRTAC1 ×2 peptides and SHBG). Considering only the biomarker (peptide) capability and prediction of person-level diagnosis, 5 peptides achieved AUC ≥0.65 for a JSN diagnosis (CRTAC1, C4BPA, LUM, CO5 and PCOC1), 1 peptide for an OST diagnosis (CRTAC1 peptides), and all 8 top peptides for a KL based diagnosis (CRTAC1 ×2 peptides, PCOC1, CO8G, LUM, COMP, CO6A3 and CO5). The highest single biomarker AUCs for diagnosis were achieved for CRTAC1 (peptide GVASLFAGR) for all definitions of osteoarthritis with AUCs for the biomarker alone ranging from 0.67-0.71 and AUCs ranging form 0.80-0.88 with addition of demographics (age, gender and BMI). One Q9NQ79_CRAC1 (CRTAC1 encoded) peptide, corresponding to Cartilage Acidic Protein 1 (aliases include ASPIC and CEP-68), an extracellular matrix protein found in cartilage, bone and lung, passed a false discovery rate (FDR) threshold (that accounts for multiple testing) of <0.01 as a diagnostic of osteoarthritis based on OST or KL grade (knee and person level) and for JSN (knee level).

TABLE 5 Top 8 peptides for prediction of knee level Diagnosis from non-depleted serum. Peptides Peptides Peptides diagnosing diagnosing diagnosing JSN (SEQ KL (SEQ OST (SEQ ID NO: X) ID NO: X) ID NO: X) Multi- KL AUCs and Multi OST AUCs and Multi- JSN AUCs and marker p values for marker p values for marker p values for

peptides

peptides AUC = 0.70 peptides AUC = 0.71

AUC = 0.77 0.71 _(BM) /0.82 _(full) CRAC1 (1) 0.68 _(BM) /0.85 _(full) CRAC1 (1) 0.74 _(BM) /0.88 _(full) CRAC1 (1) (p = 1.60E−06/0.0001) (p = 1.45E−05/0.001) (p = 7.91E−07/6.77E−05) 0.66 _(BM) /0.80 _(full) CRAC1 (2) 0.61_(BM)/0.83_(full) CXCL7 (57) 0.67 _(BM) /0.86 _(full) CRAC1 (2) (p = 5.77E−05/0.0007) (p = 0.0002/0.004) (p = 6.61E−05/0.0006) 0.62_(BM)/0.77_(full) COMP (54) 0.63_(BM)/0.83_(full) C4BPA (25) 0.65_(BM)/0.83_(full) SHBG (105) (p = 0.005/0.036) (p = 0.001/0.029) (p = 0.003/0.049) 0.63_(BM)/0.77_(full) CO6A3 (49) 0.63_(BM)/0.83_(full) COMP (54) 0.64_(BM)/0.84_(full) COMP (54) (p = 0.015/0.149) (p = 0.002/0.019) (p = 0.003/0.017) 0.63_(BM)/0.77_(full) SHBG (105) 0.63_(BM)/0.83_(full) LUM (90) 0.62_(BM)/0.85_(full) CO8G (52) (p = 0.009/0.0997) (p = 0.003/0.054) (p = 0.004/0.016) 0.59_(BM)/0.76_(full) PCOC1 (95) 0.65_(BM)/0.84_(full) CO5 (44) 0.63_(BM)/0.83_(full) PCOC1 (95) (p = 0.016/0.245) (p = 0.008/0.0515) (p = 0.006/0.118) 0.57_(BM)/0.77_(full) CO8G (52) 0.61_(BM)/0.83_(full) TIMP1 (111) 0.64_(BM)/0.83_(full) CO6A3 (49) (p = 0.052/0.203) (p = 0.004/0.018) (p = 0.015/0.120) 0.58_(BM)/0.76_(full) LUM (90) 0.64_(BM)/0.83_(full) C4BPA (26) 0.62_(BM)/0.83_(full) LUM (90) (p = 0.059/0.602) (p = 0.005/0.047) (p = 0.007/0.153)

indicates data missing or illegible when filed These multimarker AUCs are based on the top 8 peptides and cross-validated as described in the statistical methods. Data above in bold denote results passing an FDR multiple testing threshold of 0.05 to 0.01. BM is the AUC for the biomarker alone; full is the AUC for the biomarker plus demographics (age, gender, and BMI).

TABLE 6 Top 8 peptides for prediction of person level Diagnosis from non-depleted serum. Peptides Peptides Peptides diagnosing diagnosing diagnosing OST (SEQ JSN (SEQ KL (SEQ ID NO: X) ID NO: X) ID NO: X) OST AUCs and Multi- JSN AUCs and Multi- KL AUCs and Multi- p values for marker p values for marker p values for marker peptides AUC = 0.70 peptides AUC = 0.66 peptides AUC = 0.74 0.74 _(BM) /0.84 _(full) CRAC1 (1) 0.67_(BM)/0.80_(full) CRAC1 (1) 0.76 _(BM) /0.88 _(full) CRAC1 (1) (p = 1.86E−06/5.43E−05) (p = 0.0002/0.015) (p = 8.05E−07/1.42E−05) 0.69_(BM)/0.82_(full) CRAC1 (2) 0.66_(BM)/0.78_(full) C4BPA (25) 071/0.85 _(full) CRAC1 (2) (p = 0.00019/0.0009) (p = 0.002/0.014) (p = 8.37E−05/7.31E−05) 0.64_(BM)/0.78_(full) COMP (54) 0.65_(BM)/0.76_(full) LUM (90) 0.66_(BM)/0.81_(full) PCOC1 (95) (p = 0.002/0.026) (p = 0.004/0.038) (p = 0.002/0.044) 0.64_(BM)/0.77_(full) CO6A3 (49) 0.67_(BM)/0.77_(full) CO5 (44) 0.65_(BM)/0.82_(full) CO8G (52) (p = 0.0096/0.099) (p = 0.007/0.047) (p = 0.003/0.010) 0.61_(BM)/0.77_(full) PCOC1 (95) 0.66_(BM)/0.77_(full) PCOC1 (95) 0.65_(BM)/0.80_(full) LUM (90) (p = 0.017/0.208) (p = 0.006/0.101) (p = 0.008/0.139) 0.59_(BM)/0.77_(full) CO8G (52) 0.60_(BM)/0.78_(full) CXCL7 (57) 0.65_(BM)/0.81_(full) COMP (54) (p = 0.032/0.129) (p = 0.001/0.012) (p = 0.002/0.015) 0.63_(BM)/0.77_(full) ACTG (11) 0.63_(BM)/0.76_(full) COMP (54) 0.65_(BM)/0.80_(full) CO6A3 (49) (p = 0.031/0.180) (p = 0.006/0.052) (p = 0.011/0.097) 0.63_(BM)/0.76_(full) CO5 (44) 0.64_(BM)/0.78_(full) CO5 (45) 0.66_(BM)/0.80_(full) CO5 (44) (p = 0.095/0.799) (p = 0.0002/0.015) (p = 0.030/0.449) These multimarker AUCs are based on the top 8 peptides and cross-validated as described in the statistical methods. Data above in bold denote results passing an FDR multiple testing threshold of 0.05 to 0.01. BM is the AUC for the biomarker alone; full is the AUC for the biomarker plus demographics (age, gender, and BMI). 2b. Depleted Serum Proteomics (Using Original Ratios)

2b.1. Progression Analysis

TABLE 7 Top 8 peptides for prediction of knee level Progression from depleted serum. Multimarker AUCs based on top 8 peptides. Peptides predicting Peptides predicting Peptides predicting OST (SEQ ID NO: X) JSN (SEQ ID NO: X) KL (SEQ ID NO: X) Multimarker Multimarker Multimarker AUC = 0.50 AUC = 0.50 AUC = 0.50 knee level/0.61 knee and knee and person level person level person level CXCL7 (57) CO4B (43) KNG1 (88) C4BPA (25) PGCA (97) A2AP (4) C4BPB (27) FCN3 (67) CO6A3 (50) C4BPA (26) CD44 (28) A2AP (5) ITIH4 (86) TSP4(113) HGFA (78) PLMN (100) TETN (107) CO2 (41) A2AP (5) FINC (71) PRG4 (102) LYAM1 (93) ECM1 (59) DOPO (58)

2b.2. Diagnostic Analysis—

TABLE 8 Top 8 peptides for prediction of knee level Diagnosis from depleted serum. Multimarker AUCs based on top 8 peptides. Peptides diagnosing Peptides diagnosing Peptides diagnosing OST (SEQ ID NO: X) JSN (SEQ ID NO: X) KL (SEQ ID NO: X) Multimarker Multimarker Multimarker AUC = 0.74 AUC = 0.67 AUC = 0.75 knee level/0.77 knee level/0.77 knee level/0.89 person level person level person level A2AP (4) A2AP (4) A2AP (4) A2AP (5) A2AP (5) CO2 (41) CO2 (41) CO2 (41) CRAC1 (1) FA5 (61) FA5 (61) CRAC1 (2) CO5 (44) CRAC1 (1) FA5 (61) CRAC1 (1) COMP (55) CO6A3 (49) CRAC1 (2) CRAC1 (2) CO5 (44) CERU (33) HRG (79) COMP (55)

TABLE 9 Top 8 peptides for prediction of person level Diagnosis from depleted serum and using actin normalization. Multimarker AUCs based on top 8 peptides. Peptides Peptides Peptides diagnosing diagnosing diagnosing OST (SEQ ID JSN (SEQ ID KL (SEQ ID NO: X) NO: X) NO: X) Multimarker Multimarker Multimarker AUC = 0.77 AUC = 0.77 AUC = 0.89 Multimarker Multimarker Multimarker OST p AUC = 0.81 JSN p AUC = 0.78 KL p AUC = 0.88 values for with actin values for with actin values for with actin peptides normalization peptides normalization peptides normalization 7.93E−06 A2AP (4) 1.33E−07 A2AP (4) 7.83E−11 A2AP (4) 3.97E−05 A2AP (5) 5.89E−05 A2AP (5) 5.02E−06 CRAC1 (1) 4.76E−05 CRAC1 (1) 7.05E−05 CO2 (41) 1.83E−05 CO2 (41) 0.000368059 CRAC1 (2) 0.000385554 CRAC1 (1) 1.99E−05 A2AP (5) 0.000584044 CO2 (41) 0.000614719 COMP (55) 4.39E−05 CRAC1 (2) 0.001053574 FA5 (61) 0.003020118 CRAC1 (2) 0.000735498 COMP (55) 0.007678923 CO6A3 (49) 0.003279263 FA5 (61) 0.001071963 FA5 (61) 0.00822894 CO5 (44) 0.009168162 CO6A3 (49) 0.0036707 FBLN3 (64)

3. ELISA Biomarker Results

A total of 18 separate Osteoarthritis-related biomarkers were evaluated in this study:

-   -   Serum: CD44, CD163, collagen 3, COMP, haptoglobin, hemopexin,         kininogen, ceruloplasmin, hyaluronan, TBG, Vitamin D, TSG6, CD14     -   Urine: CTXIbeta, CTXIalpha, (CTX1alpha/CTXIbeta), CTX2,         ceruloplasmin, haptoglobin

The most significant results are summarized Table 10 below; these are the results for which the biomarker alone or the full model (biomarker, age, gender, BMI and cohort) achieved p<0.05. This Table lists the AUC achieved in ROC curves for the biomarker alone—AUC_(BM), and the AUC for the full model achieved for the biomarker with demographics (age, gender, BMI and cohort)—AUC_(full), and their corresponding p values.

In brief, progression markers for JSN include sHaptoglobin (knee and person level, (s) indicates serum measured), sCD44 (knee and person level), sHemopexin (knee level), and sCeruloplasmin (person level). We did not identify any strong progression markers for OST but we identified several strong markers of progression based upon KL grade change, including sHaptoglobin (knee and person level), sCD44 (knee level), and sCeruloplasmin (knee and person level). Of these, sHaptoglobin is the strongest progression marker.

Some strong diagnostic markers were identified for JSN (knee and person level) including sKininogen, sHyaluronan, sCD14, uCeruloplasmin and uCTX1alpha/CTX1beta ratio ((u) indicates urine measured). In addition, some strong diagnostic markers were identified for OST (knee and person level) including sKininogen, sCD14, and uCTX1alpha/CTX1beta ratio. Of these sKininogen and sHyaluronan are extremely strong diagnostic markers.

TABLE 10 Summary of most significant ELISA Results for Osteophyte (OST), Joint Space Narrowing (JSN), and Kellgren Lawrence (KL) grade progression and diagnostic phenotypes at a knee (black) and person-based level (red). Biomarker s = serum, u = urine PROGRESSION DIAGNOSIS (Relative amount AUC AUC for AUC for AUC for AUC for AUC for in progressors) for OST JSN KL OST JSN KL sHaptoglobin 0.62_(BM)/ 0.70_(BM)/ 0.68_(BM)/ 0.69_(BM)/ 0.59_(BM)/ (higher) 0.63_(full) 0.71_(full) 0.82_(full) 0.78_(full) 0.85_(full) (p = 0.05 (p = 0.002/ (p = 0.023/ (p = 0.0013/ (p = 0.045/ 6/0.036) 0.003) 0.021) 0.179) 0.289) 0.75_(BM)/ 0.70_(BM)/ 0.75_(full) 0.68_(full) (p = 0.001/ (p = 0.011/ 0.002) 0.015) sCD44 0.59_(BM)/ 0.57_(BM)/ (lower) 0.71_(full) 0.81_(full) (p = 0.051/ (p = 0.15/ 0.021) 0.033) 0.59_(BM)/ 0.65_(full) (p = 0.048/ 0.050) sHemopexin 0.65_(BM)/ (higher) 0.71_(full) (p = 0.018/ 0.04) sKininogen 0.67_(BM)/ 0.60_(BM)/ 0.65_(BM)/ 0.82_(full) 0.85_(full) 0.87_(full) (p = 0.000003/ (p = 0.001/ (p = 0.00004/ 0.00012) 0.073) 0.0023) (0.68_(BM)/ (0.61_(BM)/ (0.67_(BM)/ 0.82_(full)) 0.79_(full)) 0.84_(full)) (p = 0.00004/ (p = 0.001/ (p = 0.00009/ 0.0005) 0.065) 0.002) sHyaluronan 0.62_(BM)/ 0.67_(BM)/ 0.71_(BM)/ 0.71_(BM)/ (higher) 0.65_(full) 0.79_(full) 0.88_(full) 0.86_(full) (p = 0.099/ (p = 0.004/ (p = 0.001/ (p = 0.001/ 0.033) 0.133) 0.016) 0.073) (0.72_(BM)/ (0.72_(BM)/ 0.80_(full)) 0.82_(full)) (p = 0.00007/ (p = 0.00016/ 0.046) 0.069) sCD14 0.63_(BM)/ 0.65_(BM)/ 0.67_(BM)/ (higher) 0.77_(full) 0.85_(full) 0.85_(full) (p = 0.031/ (p = 0.008/ (p = 0.0008/ 0.157) 0.013) 0.043) (0.64_(BM)/ (0.70_(BM)/ (0.67_(BM)/ 0.78_(full)) 0.80_(full)) 0.82_(full)) (p = 0.018/ (p = 0.0002/ (p = 0.003/ 0.128) 0.001) 0.055) sCD163 0.58_(BM)/ 0.68_(full) (p = 0.43/ 0.046) sCeruloplas 0.63_(BM)/ 0.65_(BM)/ 0.59_(BM)/ 0.65_(BM)/ min (lower) 0.66_(full) 0.82_(full) 0.83_(full) 0.82_(full) (p = 0.032/ (p = 0.019/ (p = 0.051/ (p = 0.019/ 0.054) 0.002) 0.078) 0.002) 0.62_(BM)/ 0.64_(full) (p = 0.051/ 0.025) uCeruloplas 0.63_(BM)/ min 0.85_(full) (lower)[urine1] (p = 0.007/ 0.064) (0.66_(BM)/ 0.82_(full)) (p = 0.002/ 0.017) uCeruloplas 0.60_(BM)/ 0.65_(BM)/ 0.63_(BM)/ min 0.79_(full) 0.86_(full) 0.85_(full) (lower)[urine2] (p = 0.027/ (p = 0.004/ (p = 0.013/ 0.130) 0.129) 0.152) (0.68_(BM)/ (0.61_(BM)/ 0.82_(full)) 0.81_(full)) (p = 0.001/ (p = 0.035/ 0.031) 0.465) uCTX1alpha/ 0.62_(BM)/ 0.62_(BM)/ 0.64_(BM)/ CTX1beta 0.79_(full) 0.84_(full) 0.85_(full) (higher)[urine1] (p = 0.019/ (p = 0.017/ (p = 0.013/ 0.149) 0.63) 0.232) 0.61_(BM)/ (0.61_(BM)/ 0.79_(full) 0.82_(full)) (p = 0.045/ (p = 0.041/ 0.556) 0.779) uCTX1alpha/ 0.62_(BM)/ 0.63_(BM)/ 0.65_(BM)/ CTX1beta 0.80_(full) 0.85_(full) 0.85_(full) (higher)[urine2] (p = 0.038/ (p = 0.022/ (p = 0.025/ 0.146) 0.418) 0.165) (0.65 _(BM)/ (0.62_(BM)/ 0.80_(full)) 0.82_(full)) (p = 0.010/ (p = 0.053/ 0.257) 0.518) uCTX1 (0.63_(BM)/ 0.64_(BM)/ (higher)[urine2] 0.79_(full)) 0.85_(full) (p = 0.028/ (p = 0.050/ 0.338) 0.224) sVitamin D 0.58_(BM)/ 0.60_(BM)/ 0.62_(BM)/ binding 0.62_(full) 0.82_(full) 0.83_(full) protein (p = 0.209/ (p = 0.022/ (p = 0.0085/ (lower) 0.052) 0.797) 0.413) AUCs not underlined are knee-based results and AUCs underlined are Person-based results. BM is the AUC for the biomarker alone; full is the AUC for the biomarker plus demographics (age, gender, BMI and cohort).

Conclusions

More diagnostic than prognostic biomarkers were discovered. Non-depleted serum worked as well or better than depleted serum, therefore, the validation does not necessarily require serum depletion of the most abundant proteins. By proteomics, our strongest prognostic biomarkers were for OST and included serum CO8B and serum PLF4. By proteomics, our strongest diagnostic biomarker was serum CRTAC1 (encoding Cartilage Acidic Protein 1), which diagnosed all phenotypes of OA and passed a 1% FDR rate.

By ELISA, serum Haptoglobin was the strongest predictor of progression and predicted JSN indicative of cartilage loss. Immunoaffinity depletion of high abundance plasma proteins is frequently employed to enhance detection of lower abundance proteins in both shotgun and targeted proteomic analyses. MARS columns afford highly repeatable and efficient plasma protein depletions and a global enrichment in non-target plasma proteins of 2-4 fold. Tu et al., J Proteome Res 9(10):4982-91 (2010). We have identified serum haptoglobin to be a strong marker of knee osteoarthritis progression but it is ordinarily depleted from blood biospecimens (by both the MARS-7 and MARS-14 Human protein depletion columns by Agilent) prior to mass spectrometry analysis. Future analyses of non-depleted serum will permit addition of haptoglobin to the panel of analytes surveilled. By ELISA, serum Kininogen and serum Hyaluronan were the strongest diagnostic markers of knee OA predicting most strongly OST and JSN, respectively.

Normalization

We developed a methodology to select peptides capable of acting as normalization peptides in multiple reaction monitoring (MRM) and mass spectrometry analyses to control for intensity loading and variation of efficiency of Mars-14 protein depletion of serum or other biospecimen. We identified 14 proteins that could serve as normalization controls that are superior or equivalent to the standard methodology of normalizing to mean signal intensity. Specifically, we identified 4 normalization peptides from 4 proteins that are superior to mean intensity normalization (the standard methodology) and 21 peptides from 14 proteins that are equivalent or slightly better than standard methodology for normalization. Details are provided below. For MRM, these candidates can be run to evaluate and control for potential technical variation related to MARS-14 depletion. They might also be used in analyses of non-depleted samples to control for variation introduced by sample processing.

Table 11 below lists the proteins that are depleted by the MARS-14 column. Depletion efficiency varies across samples. This introduces variability in sample results. The standard practice is to normalize signal intensities for each peptide of interest with overall signal intensity of all peptides. Obviously, when the sample depletion has been variable, the data normalization to overall intensity will introduce variability and error in the results.

TABLE 11 List of blood proteins depleted by MARS-14. MARS Hu-14 proteins Primary Protein Name 1 albumin ALBU_BOVIN ALBU_HUMAN 2 IgG IGHG3_HUMAN 3 IgA 4 transferrin TRFE_HUMAN 5 haptoglobin HPT_HUMAN 6 antitrypsin A1AT_HUMAN 7 fibrinogen FIBA_HUMAN 8 alpha2-macroglobulin A2MG_HUMAN 9 alpha1-acid glycoprotein — 10 IgM — 11 apolipoprotein AI APOA1_HUMAN 12 apolipoprotein AII — 13 complement C3 CO3_HUMAN 14 transthyretin TTHY_HUMAN

One method we devised was to normalize based on total mean signal intensity using all signals except those emanating from any residual amounts of the proteins in the above list of proteins (that should have been depleted but that are generally depleted with varying efficiencies)—‘targeted mean total intensity’ normalization.

Our goal was to find a peptide or protein that could serve as a normalization control, i.e. a “housekeeping protein” that would eliminate the need to normalize to total mean intensity or targeted mean total intensity described above. For a normalization peptide to benefit the analysis, it should produce better signals than normalization in the standard way using mean overall intensity. Therefore, ideally, we would like to find a normalization peptide or protein that is superior to either of these methods. The test of superiority is to compare qq plots for the sample data normalized by the targeted mean total intensity (our refinement of standard practice) vs normalized to the candidate normalization protein or peptide.

TABLE 12 Normalization peptides (total of 4 peptides) that are superior to normalization by mean intensity. Peptide Superior or Teller Equivalent to Modified Peptide Sequence Proba- Primary Protein Standard Method of (SEQ ID NO:) bility Name Normalization FVFGTTPEDILR (112) 1 TSP1_HUMAN Superior ALEQDLPVNIK (40) 1 CNDP1_HUMAN Superior SEAYNTFSER (61) 0.89 FA5_HUMAN Superior IALGGLLFPASNLR (105) 0.99 SHBG_HUMAN Superior

TABLE 13 Normalization peptides (total of 21) that are slightly better or equivalent to normalization by mean intensity. Peptide Superior or Teller Equivalent to Modified Peptide Sequence Proba- Primary Protein Standard Method (SEQ ID NO:) bility Name of Normalization ICLDLQAPLYK (99) 1 PLF4_HUMAN Equivalent FQSVFTVTR (123) 1 C1QC_HUMAN Equivalent IFYNQQNHYDGSTGK (124) 1 ADIPO_HUMAN Equivalent EWVAIESDSVQPVPR (125) 0.96 CNDP1_HUMAN Equivalent SVVLIPLGAVDDGEHSQNEK 1 CNDP1_HUMAN Slightly Better (126) LVPFATELHER (127) 0.66 APOA4_HUMAN Slightly Better VAPEEHPVLLTEAPLNPK (11) 0.84 ACTG_HUMAN Equivalent FTGSQPFGQGVEHATANK 1 TSP1_HUMAN Equivalent (143) EFNPLVIVGLSK (62) 0.74 FA5_HUMAN Equivalent VLSIAQAHSPAFSCEQVR (128) 0.94 CD14_HUMAN Equivalent SITLFVQEDR (129) 0.99 TSP1_HUMAN Equivalent AEAESLYQSK (130) 0.99 K2C1_HUMAN Equivalent NALWHTGNTPGQVR (131) 0.95 TSP1_HUMAN Equivalent AIHLDLEEYR (132) 1 CNDP1_HUMAN Equivalent AGTLDLSLTVQGK (133) 0.99 TSP1_HUMAN Slightly Better EENFYVDETTVVK (134) 0.9 CBG_HUMAN Equivalent DNNSIITR (135) 0.61 CHLE_HUMAN Equivalent VVLSSGSGPGLDLPLVLGLPL 1 SHBG_HUMAN Equivalent QLK (136) DNCQYVYNVDQR (137) 0.99 TSP1_HUMAN Equivalent LFLGALPGEDSSTSFCLNGLW 0.6 SHBG_HUMAN Equivalent AQGQR (138) HNEVWHLVGITSWGEGCAQR 0.77 FA11_HUMAN Equivalent (139) Overall, the following numbers of peptides were identified as normalization controls from 14 proteins: TSP-1 (6), CNDP1 (4), FA5 (2), SHBG (3), and one each for PLF4, C1Qc, ADIPO, APOA4, ACTG, CD14, K2C1, CBG, CHLE and FA11. Three additional peptides identified in our human specimen analyses also cover 3 of these proteins, including the following:

76969249 515.7786 2 (SEQ ID NO: 140) GPDPSSPAFR TSP1_HUMAN Thrombospondin-1 OS = Homo sapiens GN = THBS1 PE = 1 SV = 2; (SEQ ID NO: 141) 76967646 520.31244 2HITSLEVIK PLF4_HUMAN Platelet factor 4 OS = Homo sapiens GN = PF4 PE = 1 SV = 2; 76968171 522.26917 (SEQ ID NO: 142) 2LDVDQALNR SHBG_HUMAN Sex hormone-binding globulin OS = Homo sapiens GN = SHBG PE = 1 SV = 2

Additional analyses have identified two other potential normalization peptides in the Carbonic anhydrase 1 protein:

CAH1_HUMAN (peptide 8856058) (SEQ ID NO: 144) GGPFSDSYR Carbonic anhydrase 1 - Homo sapiens (8848161) (SEQ ID NO: 145) GGPFSDSYR

Multimarker Analysis I Using Serum Biomarkers and Clinical Covariates

Analysis was performed to identify biomarkers that could add value for disease classification over and above clinical parameters. Serum biomarkers previously selected by the literature (E biomarkers) could modestly increase classification of Control vs. Disease (C v D). In contrast, the novel serum biomarkers (M biomarkers) discovered in the study could significantly increase this classification.

C v D (Control v Disease)

Clinical covariates could classify C v D moderately well (AUC, ca. 0.78). Biomarkers previously selected by the literature and measured by ELISA (E biomarkers) could increase classification (AUC, 0.81). In contrast, the novel M biomarkers discovered in the study could significantly increase classification (AUC, 0.97). Adding both E and M biomarkers to clinical covariates increased the AUC to 0.99.

N v P (Non-Progressor v Progressor, Person)

Clinical covariates were not significant. Adding E+M biomarkers improved classification to AUC=0.69.

PO1 v PO2 (Non-Progressor v Progressor, Osteophyte)

For clinical covariates, only gender was significant and classification was AUC=0.65. No E biomarkers were selected by the analysis. Addition of M biomarkers improved classification to AUC=0.72.

PJ1 v PJ2 (Non-Progressor v Progressor, JSN)

Clinical covariates were not significant. No M biomarkers were selected by the analysis. Adding E biomarkers improved classification to AUC=0.69.

PK1 v PK2 (Non-Progressor v Progressor, KL)

For clinical covariates, only cohort was significant for classification. No M biomarkers were selected by the analysis. Adding E biomarkers resulted in classification AUC=0.65.

General Methods Biomarker Selection

For each outcome, and each set of biomarkers (class E and class M, separately), biomarker selection was performed using the lasso selection method with the R package “glmnet”, and selecting the tuning parameter using the built in 10-fold cross-validation.

ROC Analysis

For each outcome, following biomarker selection, the observations were randomly split in half to generate a training and a test set. For the training set, two logistic regression models were fit: one with clinical parameters only (CP Training), and the other with clinical parameters+biomarkers (CP+B Training). Coefficients from the logistic regression training models were applied to the respective test sets (CP Test and CP+B Test). ROC AUC was calculated for each of the training and test sets. This process was repeated 50 times for C v D and 10 times for all other analyses and the average AUC (+/−sd) was calculated. A summary of the results is shown below in Tables 14-18.

TABLE 14 ROC AUC Clinical CP Training CP Test CP + B CP + B Test Outcome Biomarkers Set Set Training Set Set C v D E 0.802 (0.049) 0.763 (0.059) 0.946 (0.028) 0.807 (0.075) C v D M 0.822 (0.043) 0.792 (0.047)  1.00 (0.000) 0.974 (0.020) C v D E + M 0.844 (0.050) 0.793 (0.056) 1.000 (0.000) 0.990 (0.011) The numbers are the mean AUC over 10 random splits with standard deviations given in parenthesis.

TABLE 15 ROC AUC Clin- ical Bio- CP Out- mark- Training CP + B CP + B Test come ers Set CP Test Set Training Set Set N v P E 0.677 (0.087) 0.441 (0.103) 0.938 (0.074) 0.641 (0.125) N v P M 0.689 (0.056) 0.504 (0.061) 0.884 (0.033) 0.662 (0.085) N v P E + M 0.725 (0.127) 0.538 (0.080) 0.930 (0.154) 0.686 (0.070) The numbers are the mean AUC over 10 random splits with standard deviations given in parenthesis.

TABLE 16 ROC AUC Clinical CP CP + B CP + B Out- Bio- Training CP Training Test come markers Set Test Set Set Set PO1 v E no E no E no E no E PO2 biomarkers biomarkers biomarkers biomarkers selected selected selected selected PO1 v M 0.720 0.587 0.916 0.721 PO2 (0.057) (0.083) (0.052) (0.115) PO1 v E + M no E no E no E no E PO2 biomarkers biomarkers biomarkers biomarkers selected selected selected selected The numbers are the mean AUC over 10 random splits with standard deviations given in parenthesis.

TABLE 17 ROC AUC CP CP + B CP + B Clinical Bio- Training Training Test Outcome markers Set CP Test Set Set Set PJ1 v PJ2 E 0.681 0.459 0.971 0.686 (0.063) (0.094) (0.040) (0.127) PJ1 v PJ2 M no M no M no M no M biomarkers biomarkers biomarkers biomarkers selected selected selected selected PJ1 v PJ2 E + M no M no M no M no M biomarkers biomarkers biomarkers biomarkers selected selected selected selected The numbers are the mean AUC over 10 random splits with standard deviations given in parenthesis.

TABLE 18 ROC AUC CP CP + B CP + B Clinical Bio- Training Training Test Outcome markers Set CP Test Set Set Set PK1 v PK2 E 0.700 0.549 0.836 0.646 (0.053) (0.102) (0.076) (0.094) PK1 v PK2 M no M no M no M no M biomarkers biomarkers biomarkers biomarkers selected selected selected selected PK1 v PK2 E + M no M no M no M no M biomarkers biomarkers biomarkers biomarkers selected selected selected selected The numbers are the mean AUC over 10 random splits with standard deviations given in parenthesis.

Additional Information

For the C v D analysis, 19 M biomarkers were selected by the lasso method. Additional analysis was performed to identify the M biomarkers in the selection path and to test the chain of biomarkers in the path. Using 10× repeat halves sampling, it was observed that the first two biomarkers, CRAC1 (SEQ ID NO: 1) and A2AP (SEQ ID NO: 4) from depleted samples, gave an AUC of 0.948. R-scripts containing the outputs from the logistic regression analyses contain additional information for the biomarkers used in the models. The relative p-values in each model may be used to select the more significant biomarkers for that model whereby the lower p-values indicate more significant biomarkers in the model.

I. Analysis for C/D Outcomes Y=1 if D; Y=0 if C;

There are totally 126 observations with no missing in Y (89 with Y=1, 70.6%). Clinical covariates: gender, age, bmi.

I.1. Analysis Using Only Clinical Covariates.

We fit a logistic regression, and found that age and bmi are significant with p-values 0.0027 and 0.0002, respectively. The AUC (i.e. area under the ROC) is 0.7756.

I.2. Analysis Using Clinical Covariates and E Markers.

There are 19 E markers and 96 observations with complete E markers (63 with Y=1, 65.6%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 6 E markers: HA, kinno, vitd_binding, coll3, hemopexin, cd14. The AUC is 0.9192.

To evaluate the value of added E markers for prediction of C/D outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 6 E markers. Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Tables 14-18. The numbers are the mean AUC over 50 random splits with standard deviations given in parenthesis.

I.3. Analysis Using Clinical Covariates and M Markers.

There are 238 M makers and 110 observations with complete M markers (77 with Y=1, 70.0%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 19 M markers: TENX (SEQ ID NO: 106), FCGBP (SEQ ID NO: 66), C4BPB (SEQ ID NO: 27), A2AP (SEQ ID NO: 4), APOE (SEQ ID NO: 17), C1QC (SEQ ID NO: 20), CO6A3 (SEQ ID NO: 50), CRAC1 (SEQ ID NO: 1), FA5 (SEQ ID NO: 61) from depleted samples; and GELS (SEQ ID NO: 72), CXCL7 (SEQ ID NO: 57), ACTG (SEQ ID NO: 11), CFAI (SEQ ID NO: 37), CO5 (SEQ ID NO: 46), CO6A3 (SEQ ID NO: 49), CO8G (SEQ ID NO: 52), CRAC1 (SEQ ID NO: 1), FINC (SEQ ID NO: 70), PCOC1 (SEQ ID NO: 95) from nondepleted samples. The AUC is 1.

To evaluate the value of added M markers for prediction of C/D outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 19 M markers (here we use logistic regression with ridge penalty since some M markers may be highly correlated). Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 14.

I.4. The Selection Path for M Markers and Associated AUC.

The selected 19 M markers in order are: CRAC1 (SEQ ID NO: 1) (nondepleted), A2AP (SEQ ID NO: 4) (depleted), CO8G (SEQ ID NO: 52) (nondepleted), CXCL7 (SEQ ID NO: 57) (nondepleted), CO5 (SEQ ID NO: 46) (nondepleted), FCGBP (SEQ ID NO: 66) (depleted), PCOC1 (SEQ ID NO: 95) (nondepleted), CFAI (SEQ ID NO: 37) (nondepleted), CO6A3 (SEQ ID NO: 50) (depleted), GELS (SEQ ID NO: 72) (nondepleted), C4BPB (SEQ ID NO: 27) (depleted), CO6A3 (SEQ ID NO: 49) (nondepleted), ACTG (SEQ ID NO: 11) (nondepleted), CRAC1 (SEQ ID NO: 1) (deplated), FINC (SEQ ID NO: 70) (nondepleted), C1QC (SEQ ID NO: 20) (depleted), TENX (SEQ ID NO: 106) (depleted), APOE (SEQ ID NO: 17) (depeleted), FA5 (SEQ ID NO: 61) (depleted).

To evaluate the value of top k (k=1, 2, . . . , 19) selected M markers for prediction of C/D outcomes, we randomly split the data into half training and half testing datasets, and did this 10 times. The mean AUCs for testing data are given by: 0.8944272 0.9478328 0.9428793 0.9447368 0.9346749 0.9524768 0.9521672 0.9517028 0.9572755 0.9571207 0.9643963 0.9659443 0.9664087 0.9721362 0.9724458 0.9752322 0.971517 0.976161 0.977709. This suggests that the top 2 selected M markers can already improve the prediction ability sufficiently.

I.5. Analysis Using Clinical Covariates, E and M Markers.

There are 84 observations with complete E and M markers (54 with Y=1, 64.3%). The AUC based on clinical covariates plus the selected 6 E markers and 19 M markers is 1. To evaluate the value of added E and M markers for prediction of C/D outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. The results are summarized in Table 14.

II. Analysis for N/P Outcomes Y=1 if P; Y=0 if N;

There are totally 89 complete observations with P/N (66 with Y=1, 74.2%). Clinical covariates: cohort id, gender, age, bmi.

II.1. Analysis Using Only Clinical Covariates.

We fit a logistic regression, and found that none of them are significant. The AUC is 0.5975.

II.2. Analysis Using Clinical Covariates and E Markers.

There are 63 complete observations (47 with Y=1, 74.6%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 6 E markers: cd163, hapto, coll3, hemopexin, ctx2, cd14. The AUC is 0.8484.

To evaluate the value of added E markers for prediction of P/N outcomes, we randomly split the data into half training and half testing datasets, and did this 10 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 6 E markers. Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 15. The numbers are the mean AUC over 10 random splits with standard deviations given in parenthesis.

II.3. Analysis Using Clinical Covariates and M Markers.

There are 77 complete observations (59 with Y=1, 76.6%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 4 M markers: CO8B (SEQ ID NO: 51), CFAH (SEQ ID NO: 35), CRAC1 (SEQ ID NO: 1), HRG (SEQ ID NO: 81) from depleted samples. The AUC is 0.8013.

To evaluate the value of added M markers for prediction of P/N outcomes, we randomly split the data into half training and half testing datasets, and did this 10 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 4 M markers. Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 15.

II.4. Analysis Using Clinical Covariates, E and M Markers.

There are 54 complete observations (41 with Y=1, 75.9%). The AUC based on clinical covariates plus the selected 6 E markers and 4 M markers is 0.8949. To evaluate the value of added E and M markers for prediction of P/N outcomes, we randomly split the data into half training and half testing datasets, and did this 10 times. The results are summarized in Table 15.

III. Analysis for P/O Outcomes Y=1 if PO2; Y=0 if PO1;

There are totally 82 complete observations with PO1/PO2 (54 with Y=1, 65.85%). Clinical covariates: cohort id, gender, age, bmi.

III.1. Analysis Using Only Clinical Covariates.

We fit a logistic regression, and found that only gender is significant. The AUC is 0.6548.

III.2. Analysis Using Clinical Covariates and E Markers.

There are 61 complete observations (41 with Y=1, 67.2%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. None of the E markers are selected.

III.3. Analysis Using Clinical Covariates and M Markers.

There are 71 complete observations (47 with Y=1, 66.2%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 5 M markers: LYAM1 (SEQ ID NO: 93) from depleted samples, KLKB1 (SEQ ID NO: 87), CXCL7 (SEQ ID NO: 57), CO8B (SEQ ID NO: 51), ANT3 (SEQ ID NO: 14) from nondepleted samples. The AUC is 0.8555.

To evaluate the value of added M markers for prediction of P/O outcomes, we randomly split the data into half training and half testing datasets, and did this 10 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 5 M markers. Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 16.

IV. Analysis for P/J Outcomes Y=1 if PJ2; Y=0 if PJ1;

There are totally 74 complete observations with PJ1/PJ2 (40 with Y=1, 54.1%). Clinical covariates: cohort id, gender, age, bmi.

IV.1. Analysis Using Only Clinical Covariates.

We fit a logistic regression, and found that none of them are significant. The AUC is 0.6279.

IV.2. Analysis Using Clinical Covariates and E Markers.

There are 52 complete observations (27 with Y=1, 51.9%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 6 E markers: hapto, vitd_binding, cd44, hemopexin, cerulo_serum, ctx2. The AUC is 0.9185.

To evaluate the value of added E markers for prediction of P/N outcomes, we randomly split the data into half training and half testing datasets, and did this 10 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 6 E markers. Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 17.

IV.3. Analysis Using Clinical Covariates and M Markers.

There are 66 complete observations (37 with Y=1, 56.1%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. None of the M markers are selected.

V. Analysis for P/K Outcomes Y=1 if PK2; Y=0 if PK1;

There are totally 89 complete observations with PK1/PK2 (31 with Y=1, 34.8%). Clinical covariates: cohort id, gender, age, bmi.

V.1. Analysis Using Only Clinical Covariates.

We fit a logistic regression, and found that only cohort id is significant. The AUC is 0.7269.

V.2. Analysis Using Clinical Covariates and E Markers.

There are 63 complete observations (18 with Y=1, 28.6%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 1 E marker: hapto. The AUC is 0.7605.

To evaluate the value of added E markers for prediction of P/N outcomes, we randomly split the data into half training and half testing datasets, and did this 10 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 1 E marker. Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 18.

V.3. Analysis Using Clinical Covariates and M Markers.

There are 77 complete observations (28 with Y=1, 36.4%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. None of the M markers are selected.

Multimarker Analysis II Using Serum Biomarkers and Clinical Covariates Analysis Based on M Markers: M141-M257 I. Analysis for C/D Outcomes Y=1 if D; Y=0 if C; I.0. Selection Based on M Markers Only.

There are totally 118 observations with no missing in Y (83 with Y=1, 70.3%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 5 M markers: CXCL7 (SEQ ID NO: 57), CO6A3 (SEQ ID NO: 49), CO8G (SEQ ID NO: 52), CRAC1 (SEQ ID NO: 1), COMP (SEQ ID NO: 54) from nondepleted samples.

I.1. Analysis Using Clinical Covariates and M Markers.

To evaluate the value of added M markers for prediction of C/D outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 5 M markers (here we use logistic regression with ridge penalty since some M markers may be highly correlated). Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 19 below:

TABLE 19 Training Data Testing Data Clinical + M markers 0.908 (0.032) 0.851 (0.047)

I.2. Analysis Using Clinical Covariates, E and M Markers.

To evaluate the value of added E and M markers for prediction of C/D outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. The results are summarized in Table 20 below:

TABLE 20 Training Data Testing Data Clinical + E + M markers 0.969 (0.022) 0.893 (0.038)

II. Analysis for N/P Outcomes Y=1 if P; Y=0 if N; II.0. Selection Based on M Markers Only.

There are totally 83 observations with no missing in Y (62 with Y=1, 74.7%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 10 M markers: PLF4 (SEQ ID NO: 99), CO8B (SEQ ID NO: 51), CERU (SEQ ID NO: 29), CFAH (SEQ ID NO: 35), FINC (SEQ ID NO: 70), HRG (SEQ ID NO: 81), PRG4 (SEQ ID NO: 101), SAMP (SEQ ID NO: 104), TSP4 (SEQ ID NO: 113) from nondepleted samples.

II.1. Analysis Using Clinical Covariates and M Markers.

To evaluate the value of added M markers for prediction of P/N outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 10 M markers (here we use logistic regression with ridge penalty since some M markers may be highly correlated). Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 21 below:

TABLE 21 Training Data Testing Data Clinical + M markers 0.902 (0.078) 0.726 (0.080)

II.2. Analysis Using Clinical Covariates, E and M Markers.

To evaluate the value of added E and M markers for prediction of P/N outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. The results are summarized in Table 22 below:

TABLE 22 Training Data Testing Data Clinical + E + M markers 0.937 (0.100) 0.739 (0.100)

III. Analysis for P/O Outcomes Y=1 if PO2; Y=0 if PO1; III.0. Selection Based on M Markers Only.

There are totally 76 complete observations with PO1/PO2 (50 with Y=1, 65.8%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. It selects 7 M markers: PLF4 (SEQ ID NO: 99), KLKB1 (SEQ ID NO: 87), CO8B (SEQ ID NO: 51), ANT3 (SEQ ID NO: 14), ACTG (SEQ ID NO: 11), CD44 (SEQ ID NO: 28), CFAI (SEQ ID NO: 37) from nondepleted samples.

III.1. Analysis Using Clinical Covariates and M Markers.

To evaluate the value of added M markers for prediction of PO1/PO2 outcomes, we randomly split the data into half training and half testing datasets, and did this 50 times. Based on the training data, we fit two logistic regression: (i) using the clinical covariates only; (ii) using the clinical covariates plus the selected 7 M markers (here we use logistic regression with ridge penalty since some M markers may be highly correlated). Then, we applied the fitted scores to both training and testing data to compute AUC. The results are summarized in Table 23 below:

TABLE 23 Training Data Testing Data Clinical + M markers 0.900 (0.052) 0.776 (0.059)

III.2. Analysis Using Clinical Covariates, E and M Markers.

No E markers were selected.

IV. Analysis for P/J Outcomes Y=1 if PJ2; Y=0 if PJ1; IV.0. Selection Based on M Markers Only.

There are totally 70 complete observations with PJ1/PJ2 (50 with Y=1, 54.3%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. No M markers were selected.

V. Analysis for P/K Outcomes Y=1 if PK2; Y=0 if PK1; IV.0. Selection Based on M Markers Only.

There are totally 83 complete observations with PK1/PK2 (30 with Y=1, 36.1%). We conducted lasso selection using the R package “glmnet”, and selected the tuning parameter using the built-in 10-fold cross-validation. No M markers were selected.

Multimarker Analysis III Using Serum Biomarkers Methods

We generated further multi-marker models based on the markers we identified. Inferential methods and predictive methods were used depending on the structure of the data. For a person-level analysis, logistic regression was used to compute p-values and confidence intervals. Covariates included age, sex, BMI, and cohort. The effect of a biomarker was added to a model containing these covariates and a likelihood-ratio test was used to assess the significance of the biomarker after accounting for the covariates. A biomarker was considered significant if it surpassed a Benjamini-Hochberg FDR threshold of 10%. For a knee-level analysis the dependence arising from paired observations must be considered. We used the generalized estimating equation (GEE) method to account for the correlation structure. A biomarker was added to a model containing base covariates and its significance was assessed by a Wald statistic.

Predictive models were used to assess discrimination through the AUC. We used feature selection coupled with ridge regression, a form of penalized regression, for all models implemented in the glmnet R package. Penalized regression is often used for predictive models to constrain the size of coefficients to lessen the effects of overfitting the data. Feature selection consisted of selecting the top 8 markers with the lowest p-values, which is a simple but effective method for the numbers of peptides in the current data set. Leave-one-out cross-validation was used in which selecting tuning parameters and carrying out feature selection was repeated at each iteration of cross-validation to mimic the process of fitting a model to new data. In sum, all multimarker AUCs have been properly cross-validated. For knee-level (paired) analysis, the leave-one-out cross-validation scheme was modified to a leave-sample-out scheme so that the test set was independent of the training set.

Results

A summary of the results is shown in Tables 24-29.

Depleted Serum—Multimarker Analyses; Dx=Diagnosis (Predict Knee OA Diagnosis); Px=Prognosis (Predict Knee OA Progression).

TABLE 24 p value adjusted p value peptide for age, gender, alone BMI and cohort JSN (SEQ ID NO: X) (pval_pep_only) (pval_base_full) knee JSN Dx depleted AUC = 0.77 A2AP (4) 9.26E−05 0.046613974 A2AP (5) 2.42E−05 0.142711257 CO2 (41) 9.76E−05 0.078527954 COMP (55) 0.001556863 0.124035832 FA5 (61) 0.002710746 0.209278011 CO5(44) 0.009185471 0.865395357 CRAC1 (1) 0.018818294 0.011011104 SHBG (105) 0.014916307 0.803569832 knee JSN person Dx depleted AUC = 0.80 A2AP (4) 3.31E−07 0.008051178 A2AP (5) 2.81E−05 0.292406073 CO2(41) 8.72E−05 0.162905105 COMP (55) 0.001303611 0.180539523 CRAC1 (1) 0.003128843 0.006690407 FA5 (61) 0.005274008 0.367965374 CO5(44) 0.022968897 0.863307127 CRAC1 (2) 0.024934734 0.051344408 knee JSN Px depleted AUC = 0.41 CO4B (43) 0.033584489 0.054265435 FCN3 (67) 0.076630893 0.020166429 CO8B (51) 0.017661819 0.011132744 FINC (70) 0.040090816 0.045404495 PGCA (97) 0.065006579 0.105883213 TSP4 (113) 0.076843539 0.02082132 FINC (71) 0.051256407 0.091823154 TETN (107) 0.05530156 0.041645638 knee JSN person Px depleted AUC = 0.39 PGCA (97) 0.026293441 0.046827678 CO4B (43) 0.023475468 0.015862788 TENX (106) 0.039475824 0.033732372 FCN3 (67) 0.18678107 0.107948938 C4BPA (25) 0.064638445 0.030266734 TSP1 (112) 0.13133037 0.18210438 CO8B (51) 0.113256944 0.07456324 HRG (79) 0.1438498 0.062444406

TABLE 25 p value adjusted for age, gender, p value peptide BMI and alone cohort KL (SEQ ID NO: X) (pval_pep_only) (pval_base_full) knee KL Dx depleted AUC 0.82 A2AP (4) 7.71E−05 0.00276514 CO2 (41) 5.90E−05 0.002320056 A2AP (5) 8.93E−06 0.062148737 FAS (61) 0.000470793 0.058126283 COMP (55) 0.001082738 0.12106214 CO5 (44) 0.000562536 0.229466647 CRAC1 (1) 0.004460634 0.001449587 CRAC1 (2) 0.033778439 0.009978404 knee KL person Dx depleted AUC = 0.89 A2AP (4) 1.50E−07 0.000135815 A2AP (5) 9.64E−05 0.140576823 CRAC1 (1) 0.000106179 6.44E−05 CO2 (41) 0.00023168 0.024410373 CRAC1 (2) 0.001782088 0.00062291 COMP (55) 0.006752536 0.186567751 FA5 (61) 0.00570514 0.108817433 CO5 (44) 0.019947104 0.406134156 knee KL Px depleted AUC = 0.43 A2AP (4) 0.038108974 0.38563753 KNG1 (88) 0.020411121 0.391748644 HGFA (78) 0.037729146 0.03308354 PRG4 (102) 0.128488782 0.333151487 AFAM (12) 0.120643826 0.844521484 DOPO (58) 0.13172217 0.264987362 HNC (71) 0.068344045 0.133741621 CO8B (51) 0.062727522 0.03532693 knee KL person Px depleted AUC = 0.39 KNG1 (88) 0.052000668 0.315327177 HGFA (78) 0.054701983 0.056927485 A2AP (4) 0.10641075 0.563510596 FAS (61) 0.140793944 0.673707755 TSP1 (112) 0.148443778 0.390538592 PGCA (97) 0.135584259 0.183730223 TENX (106) 0.149867446 0.055623532 FINC (71) 0.164143337 0.229058614

TABLE 26 p value adjusted p value peptide for age, gender, alone BMI and cohort OST (SEQ ID NO: X) (pval_pep_only) (pval_base_full) knee OST Dx depleted AUC = 0.66 A2AP (5) 3.35E−05 0.022657938 A2AP (4) 0.001538871 0.021017782 FA5 (61) 0.000257061 0.006428595 CO2(41) 0.002198765 0.036511365 CO5(44) 0.001100287 0.024126793 COMP (55) 0.004918696 0.096093869 SHBG (105) 0.005156474 0.10532715 CO6A3 (49) 0.008366857 0.086848008 knee OST Dx person depleted AUC = 0.82 A2AP (5) 2.09E−05 0.017678836 A2AP (4) 6.34E−05 0.01810586 CO2 (41) 0.000414071 0.024973599 FAS (61) 0.001001698 0.014610139 CRAC1 (1) 0.002263888 0.007041871 CO5 (44) 0.006050591 0.091904821 COMP (55) 0.009743019 0.152299519 CRAC1 (2) 0.02066252 0.028329279 knee OST Px depleted AUC = 0.49 CXCL7 (57) 0.003370215 0.003050257 C4BPA (25) 0.003682397 0.005664358 C4BPB (27) 0.003909035 0.00385861 C4BPA (26) 0.006334513 0.007556145 A2AP (4) 0.038641621 0.029667601 ITIH4 (86) 0.041117212 0.033451744 PLMN (100) 0.042208729 0.03218861 HRG (83) 0.033182707 0.01429709 knee OST person Px depleted AUC = 0.54 C4BPA (25) 0.011047566 0.011004835 C4BPB (27) 0.014086851 0.010277587 C4BPA (26) 0.011958263 0.012290566 CXCL7 (57) 0.013978919 0.015727039 LYAM1 (93) 0.071115015 0.024706069 A2AP (4) 0.126628387 0.024897652 TSP1 (112) 0.101650546 0.03394991 FINC (71) 0.099794158 0.146642438 Non-Depleted Serum - multimarker analyses; Dx = diagnosis (predict knee OA diagnosis); Px = prognosis (predict knee OA progression)

TABLE 27 p value adjusted p value peptide for age, gender, alone BMI and cohort JSN (SEQ ID NO: X) (pval_pep_only) (pval_base_full) knee JSN Dx nondepleted AUC = 0.71 CRAC1 (1) 1.45E−05 0.001334661 CXCL7 (57) 0.000220209 0.004033964 C4BPA (25) 0.001092182 0.029609534 COMP (54) 0.001921796 0.019441399 LUM (90) 0.003127373 0.053918866 CO5 (44) 0.007557254 0.051506718 TIMP1 (111) 0.003709593 0.017775522 C4BPA (26) 0.005168882 0.046877637 knee JSN person Dx nondepleted AUC = 0.66 CRAC1 (1) 0.00026204 0.014921219 C4BPA (25) 0.002059188 0.013830002 LUM (90) 0.003509795 0.038433173 CO5 (44) 0.007188587 0.047295814 PCOC1 (95) 0.006323501 0.10111207 CXCL7 (57) 0.001410986 0.011528214 COMP (54) 0.005892446 0.051513052 CO5 (45) 0.010430159 0.054225697 knee JSN Px nondepleted AUC = 0.55 PGCA (96) 0.018643447 0.076340059 APOH (18) 0.029178909 0.015538341 SAMP (104) 0.011280848 0.026954743 AACT (10) 0.028545301 0.044498503 AACT (8) 0.020730449 0.02340926 AACT (9) 0.015384508 0.02138342 CFAH (35) 0.030877701 0.008199734 PHLD (98) 0.065419311 0.075331479 TSP1 (112) 0.040324672 0.139428301 THRB (110) 0.153796326 0.031114401 knee JSN person Px nondepleted AUC = 0.45 TSP1 (112) 0.020309869 0.038161024 CFAH (35) 0.025234989 0.015467182 THRB (110) 0.025437875 0.026717885 HRG (81) 0.05431721 0.01105114 APOH (18) 0.048284307 0.028696354 AACT (8) 0.064355392 0.042526395 PHLD (98) 0.054354097 0.079726494 AACT (9) 0.079176188 0.058988993

TABLE 28 p value adjusted p value peptide for age, gender, alone BMI and cohort KL (SEQ ID NO: X) (pval_pep_only) (pval_base_full) knee KL Dx nondepleted AUC = 0.77 CRAC1 (1) 7.91E−07 6.77E−05 CRAC1 (2) 6.61E−05 0.000611469 SHBG (105) 0.002780496 0.049018578 COMP (54) 0.003119022 0.016939944 CO8G (52) 0.004439784 0.01618559 PCOC1 (95) 0.005867991 0.118372629 CO6A3 (49) 0.014832263 0.120147034 LUM (90) 0.006899035 0.152788954 knee KL person Dx nondepleted AUC = 0.74 CRAC1 (1) 8.05E−07 1.42E−05 CRAC1 (2) 8.37E−05 7.31E−05 PCOC1 (95) 0.001985962 0.044267004 CO8G (52) 0.003214569 0.010196682 LUM (90) 0.007570064 0.138934545 COMP (54) 0.002099819 0.015488264 CO6A3 (49) 0.0110763 0.097029625 CO5 (44) 0.03014129 0.449289979 knee KL Px nondepleted AUC = 0.43 CFAH (35) 0.003413853 0.003720699 SAMP (104) 0.008874549 0.01757112 TSP1 (112) 0.016396723 0.029173098 HEP2 (76) 0.021317466 0.138072515 C1R (22) 0.022324877 0.035342549 APOB (15) 0.019423193 0.073310656 FINC (71) 0.038839588 0.741216237 HEP2 (77) 0.061398201 0.173258732 PGCA (96) 0.071967152 0.438930963 FINC (70) 0.044013606 0.329880745 knee KL person Px nondepleted AUC = 0.57 CFAH (35) 0.004647535 0.004744558 TSP1 (112) 0.014072412 0.044301897 SAMP (104) 0.007169693 0.038267836 APOB (15) 0.026721333 0.127645336 AACT (9) 0.014781295 0.049356758 AACT (8) 0.017046792 0.061084614 C1R (22) 0.031947667 0.082794302 C1R (23) 0.030621922 0.095021007

TABLE 29 p value adjusted p value peptide for age, gender, alone BMI and cohort OST (SEQ ID NO: X) (pval_pep_only) (pval_base_full) knee OST Dx nondepleted AUC = 0.70 CRAC1 (1) 1.60E−06 0.00013426 CRAC1 (2) 5.77E−05 0.0007071 COMP (54) 0.004838815 0.036180336 CO6A3 (49) 0.015033517 0.149650103 SHBG (105) 0.009303617 0.099766058 PCOC1 (95) 0.016164645 0.244959617 CO8G (52) 0.052018531 0.202674481 LUM (90) 0.058692947 0.601716961 knee OST Dx person nondepleted AUC = 0.70 CRAC1 (1) 1.86E−06 5.43E−05 CRAC1 (2) 1.86E−06 5.43E−05 COMP (54) 0.002304261 0.025690528 CO6A3 (49) 0.009634809 0.098819568 PCOC1 (95) 0.016841954 0.207971822 CO8G (52) 0.031580042 0.128396316 ACTG (11) 0.030554232 0.180275486 CO5 (44) 0.095059051 0.79862693 knee OST Px nondepleted AUC = 0.61 PLF4 (99) 0.001094118 0.001590437 CXCL7 (57) 0.009976718 0.012612223 ANT3 (14) 0.011884039 0.006241664 AACT (8) 0.00821959 0.002747701 AACT (10) 0.010576976 0.002088226 THRB (110) 0.030760839 0.053273043 AACT (9) 0.016720482 0.0084448 ITIH4 (86) 0.039793857 0.04003038 CO8B (51) 0.061264758 0.062748503 PLMN (100) 0.046320809 0.044378072 knee OST person Px nondepleted AUC = 0.67 CO8B (51) 0.00274445 0.007382756 PLF4 (99) 0.003710526 0.005376922 PRG4 (101) 0.016806589 0.059964262 PRG4 (102) 0.024390935 0.054916368 ANT3 (14) 0.017572544 0.019097753 C4BPA (26) 0.029402337 0.122776322 CXCL7 (57) 0.005698 0.006885551 C4BPA (25) 0.051311885 0.186676691 

1. A method comprising measuring an expression level of at least one biomarker selected from the group consisting of CRAC1 (CRTAC1), CXCL7, CO8G, A2AP, A1BG, A2GL, AACT, ACTG, AMBP, APOB, APOE, B2MG, C1QC, C1R, C1RL, C4BPA, C4BPB, CD14, CD44, CERU, CFAB, CFAH, CFAI, CILP1, C1S, CNDP1, CO2, CO4B, CO5, CO6A3, CO8B, CO9, coll3, COMP, CTX1a, CTX1b, CTX2, CTXi, ECM1, FA12, FA5, FBLN1, FBLN3, FCGBP, FCN3, FETUA, FINC, GELS, HA, HABP2, haptoglobin, HEMO, HEP2, HGFA, HRG, hyaluronan, IC1, ITIH1, ITIH4, KNG1, LAMA2, LUM, LYAM1, MASP1, PCOC1, PGCA, PHLD, PLF4, PLMN, PRG4, RET4, SAMP, SHBG, TENX, TETN, THBG, TIMP1, TSP1, TSP4, VTDB, VTNC, ZA2G, ZPI, or any combination thereof in a sample from a subject; diagnosing the subject with osteoarthritis if the level of at least one biomarker is altered as compared to a reference level of the biomarker; and administering an anti-inflammatory or anti-pain therapeutic to the subject if the subject is diagnosed with osteoarthritis.
 2. The method of claim 1, wherein the biomarkers measured comprise at least one biomarker selected from the group consisting of CRAC1 (CRTAC1), CXCL7, CO8G, ACTG, CD44, CERU, CFAH, CFAI, CO6A3, CO8G, COMP, FINC, HRG, KNG1, PLF4, PRG4, SAMP, TSP4, and any combination thereof.
 3. The method of claim 2, wherein the biomarkers measured comprise at least two biomarkers selected from the group consisting of CRAC1 (CRTAC1), CXCL7, and CO8G.
 4. The method of claim 3, wherein the biomarkers measured comprise CRAC1 (CRTAC1), CXCL7, and CO8G.
 5. The method of claim 3, wherein the subject is diagnosed with osteoarthritis if the levels of the biomarkers measured are increased as compared to the reference level.
 6. The method of claim 3, wherein the at least two biomarkers comprise a peptide sequence selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2 (CRAC1 (CRTAC1)), SEQ ID NO: 57 (CXCL7), SEQ ID NO: 52 (CO8G).
 7. (canceled)
 8. (canceled)
 9. The method of claim 1, wherein the anti-inflammatory or anti-pain therapeutic comprises a nonsteroidal anti-inflammatory drug (NSAID).
 10. A method comprising measuring an expression level of at least one biomarker selected from the group consisting of CRAC1 (CRTAC1), A1BG, A2AP, A2GL, AACT, ACTG, AFAM, ANT3, APOB, APOH, B2MG, C1QC, C1R, C1RL, C4BPA, C4BPB, CD14, CD163, CD44, CERU, CFAB, CFAH, CFAI, C1S, CO2, CO4B, CO5, CO6A3, CO8B, CO8G, coll3, CTX2, CXCL7, DOPO, ECM1, FA5, FA12, FBLN1, FCGBP, FCN3, FETUA, FINC, GELS, HABP2, haptoglobin, HEMO, HEP2, HGFA, HRG, hyaluronan, ITIH4, KLKB1, KNG1, LUM, LYAM1, PGCA, PHLD, PLF4, PLMN, PRG4, RET4, SAMP, TENX, TETN, THBG, THRB, TIMP1, TSP1, TSP4, VTDB, VTNC, or combinations thereof in a sample from a subject; predicting the progression of the osteoarthritis, wherein altered levels of any of the biomarkers as compared to the reference level is indicative of progression of disease; and administering an anti-inflammatory or anti-pain therapeutic to the subject if the subject is diagnosed with osteoarthritis.
 11. The method of claim 10, wherein the biomarkers measured comprise at least one biomarker selected from the group consisting of ACTG, ANT3, CD44, CERU, CFAH, CFAI, CO8B, CXCL7, CO6A3, CRAC1 (CRTAC1), FINC, haptoglobin, HRG, KLKB1, PLF4, PRG4, SAMP, TSP4 and any combination thereof.
 12. The method of claim 11, wherein the biomarkers measured comprise at least two biomarkers selected from the group consisting of PLF4, CFAH, and ANT3.
 13. The method of claim 12, wherein the biomarkers measured comprise PLF4, CFAH, and ANT3.
 14. The method of claim 12, wherein a prediction of progression of the osteoarthritis in the subject is made if the level of the at least two biomarkers is altered as compared to the reference level as follows: ANT3 levels are decreased, CFAH levels are increased or PLF4 levels are increased.
 15. The method of claim 12, wherein the at least two biomarkers comprise a peptide sequence selected from the group consisting of SEQ ID NO: 14 (ANT3), SEQ ID NO: 35 (CFAH), SEQ ID NO: 99 (PLF4).
 16. (canceled)
 17. (canceled)
 18. The method of claim 10, wherein the anti-inflammatory or anti-pain therapeutic comprises a nonsteroidal anti-inflammatory drug (NSAID).
 19. The method of claim 1, wherein the biomarker is measured using an antibody-based capture method or mass spectrometry.
 20. (canceled)
 21. (canceled)
 22. (canceled)
 23. The method of claim 1, wherein the sample is serum, plasma, urine, or synovial fluid.
 24. The method of claim 23, wherein the sample is serum depleted of at least 7 major serum proteins.
 25. The method of claim 24, wherein the serum proteins depleted are selected from the group consisting of albumin, IgG, IgA, transferrin, haptoglobin, anti-trypsin, fibrinogen, alpha 2-macroglobulin, IgM, apolipoprotein AI, apolipoprotein AII, complement C3, and transthyretin.
 26. The method of claim 1, further comprising measuring the level of at least one normalization peptide from a protein selected from TSP1, CNDP1, FA5, SHBG, PLF4, C1QC, ADIPO, APOA4, ACTG, CD14, K2C1, CBG, CHLE, FA11, CAH1 or any combination thereof in a sample from a subject and normalizing the level of the biomarker in the sample from the subject and the reference level of the biomarker to the level of the normalization peptide in the sample and the reference prior to comparing the level of the biomarker in the sample to the reference level of the biomarker.
 27. The method of claim 26, wherein the normalization peptide is selected from the group consisting of SEQ ID NO: 40, SEQ ID NO: 61, SEQ ID NO: 105, and SEQ ID NO:
 112. 